Aptitude Tests

Stanford Binet Intelligence Scales, Fifth Edition (SB5; [21 ]). The SB5 is a standardized test of intellectual aptitude for children and adults between ages 2 to 85 years. The fifth edition was developed and structured based on the Cattell-Horn-Carroll (CHC; [22 ]) theory of intelligence. The CHC model conceptualizes intelligence as having a hierarchical structure with three levels: narrow abilities at the lowest level, broad cognitive abilities in the middle and a general measure of cognitive ability (g) at the highest level.
The SB5 provides a general ability score reported as the FSIQ, and five index scores that measure the broad cognitive concepts of Fluid Reasoning (FR), Knowledge (KN), Quantitative Reasoning (QR), Visual Spatial Processing (VS), and Working Memory (WM). These five indices are measured across two broad response domains, Verbal (VIQ) and Non-verbal (NVIQ), in total providing ten subtest scores. These subtests (with the exception of two routing subtests) are measured across five or six testlets that vary in level of difficulty. Each testlet has a range of possible raw scores from 0 to 6 and is made up of 3 to 6 items. Raw scores from testlets within the same subtest are summed together and then transformed into a scaled score with a mean of 10 and standard deviation of 3 based on SB5 normative data. Normative data is based on a standardization sample of 4,800 individuals stratified by age, sex, race/ethnic group, geographical region, and socio-economic status. The subtest scaled scores are then combined and translated into index scores and the three intelligence quotients (VIQ, NVIQ, and FSIQ). The SB5 introduced a new scoring method for deriving an extended IQ score (EXIQ) that broadened the range of scores from 40 to 160 to 10 to 225. For EXIQ, using the one-parameter Rasch model, the total raw scores were converted into a change sensitive score (CSS). Using traditional methods, norms for the CSS score were calculated for all 30 age groups and then re-scaled to the IQ metric.

If not provided in the article, the contingency table of true positives, false negatives, true negatives and false positives was constructed for each cut-off point assessed based on the available information, usually sensitivity, specificity and prevalence of the disorder according to the gold standard.
The suggested cut-point for the scale is 16, but many studies presented accuracy results of the CES-D using other cut-points, since they aimed to evaluate and compare their performance and select the optimal value in that specific population. However, for the assessment of overall performance of the scale using information from all the 28 studies, we selected results for only one cut-off point per study, so that each study contributes only one estimate of sensitivity and specificity as required by the statistical methods applied[21 ]. We chose the cut-off point of 16 whenever possible, as this is the value usually recommended for the detection of depression with the CES-D. Notwithstanding, when a study did not report diagnostic accuracy results for cut-off 16, we used the cut-off point reported in that particular study. When more than one cut-off point was reported in an article, and in order to avoid multiple testing effects, we selected the cut-off with the best diagnostic accuracy within the study. We obtained the coupled forest plot reporting the raw data consisting of the 2x2 sensitivity and specificity table from each study, as well as the estimated sensitivity (SN; the proportion of true cases correctly classified by the cut-off point) and specificity (SP; the proportion of true non-cases correctly classified) for detection of depression of each of the studies, together with 95% confidence intervals. In the context of meta-analysis, when a variety of sensitivity and specificity values for a given test are available from several independent studies depending on the cut-off point, the summary receiver operating characteristic (SROC) has been proposed as a way to assess diagnostic data [25 (link)]. The SROC curve considers both sensitivity and specificity and the relationship between them, taking into account that not all studies used the same cut-off. It is assumed that different values of sensitivity and specificity apply if the cut-off point defining a positive test result varies from study to study, everything else being equal. Several procedures have been proposed to estimate the SROC curve from a set of independent studies [25 (link)–27 (link)]. Here, the Rutter and Gatsonis mixed effects model [26 (link)] was fitted to estimate the SROC curve, and the sensitivity and specificity of each study, weighted by study size were plotted in the ROC space. The area under the curve (AUC) for the fitted SROC was computed from the estimated diagnostic odds ratio (DOR) following the method described by Walter [28 (link)]. Also, for the subsample of studies that provided diagnostic accuracy results for the cut-off point 16 (n = 22), we estimated a bivariate meta-regression [27 (link)], which allowed us to obtain pooled estimates of a range of summary performance measures of the test’s ability to detect the presence of a disease for a given cut-off point. Specifically, the summary measures obtained were: a) specificity and sensitivity, and their corresponding 95% confidence intervals; b) the positive likelihood ratio (LR+) that described how many times more likely positive test results were in the diseased group compared to the non-diseased group; c) the negative likelihood ratio (LR-), describing how many times less likely negative test results were in the diseased group compared to the non-diseased group; and d) the DOR, that summarizes the diagnostic accuracy of the test as a single number describing how many times higher the odds are of obtaining a positive test result in a diseased rather than in a non-diseased person. Additionally, we evaluated the screening accuracy of other cut-off points that were assessed in a minimum of 6 studies using the same methodology. In this case, a separate bivariate model was estimated for each of the cut-off points, and each study could contribute to one or more of the models depending on what cut-off points it reported [21 ].
The following variables were assessed as possible sources of heterogeneity: a) the study setting; b) the measure used as the gold standard; c) the version of the instrument (English versus cultural adaptation); d) the age group of the study sample; e) disorder prevalence; and f) specific QUADAS items for which more than 20% of the studies presented problems. Heterogeneity was evaluated with the Rutter and Gatsonis mixed effects models (see above) including each covariate at a time and testing its statistical significance with the likelihood ratio test. Estimates of model parameters were obtained using the METADAS macro [29 ] implemented in SAS (SAS v9.1.2) [30 ].

Participants first underwent extensive training to learn the transition matrix (Figure 2A,B; [16] (link)). During the training, subjects were repeatedly placed in a random starting state and told to reach a random target state in a specified number of moves (up to 4). After 40 practice trials, training continued until the participant reached the target in 9 out of 10 trials. Most subjects passed the training criterion in three attempts. Reaching training criterion was mandatory to move on to the main task.
After training, each transition was associated with a deterministic reward (Figure 2B). Subjects completed two blocks of of 24 choice episodes; each episode included 2 to 8 trials. The first block of 24 episodes was discarded as part of training the reward matrix, and the second block of 24 episodes was analysed. At the beginning of each episode, subjects were placed randomly in one of the states (highlighted in white) and told how many moves they would have to make (i.e., 2 to 8). Their goal was to devise a sequence of that particular length of moves to maximize their total reward over the entire sequence of moves. To help the subjects remember the reward or punishment possible from each state, the appropriate “+” or “-” were always displayed beneath each box. Regardless of the state the subject finished in on a given episode, they would be placed in a random new state at the beginning of the next episode. Thus, each episode was an independent test of the subject's ability to sequentially think through the transition matrix and infer the best action sequence. After each transition, the new state was highlighted in white and the outcome displayed. On half of the trials, subjects were asked to plan ahead their last 2–4 moves together and enter them in one step without any intermittent feedback.
The reward matrix was designed to assess subjects' pruning strategy; and whether this strategy changed in an adaptive, goal-directed way. All subjects experienced the same transition matrix, but the red transitions in Figure 2C led to different losses in the three groups, of −70, −100 or −140 pence respectively. This had the effect of making pruning counterproductive in groups −70 and −100, but not −140 (Figures 2C–E). At the end of the task, subjects were awarded a monetary amount based on their performance, with a maximum of £20. They were also compensated £10 for time and travel expenses.

Bacillus subtilis of TLRI 211-1 was screened from the activated sludge at TLRI. After species identification, high temperature resistance (50°C), and spore-producing ability test, the B. subtilis was selected and inoculated in Tryptic Soy Broth culture media and placed in a 30°C incubator for 24 hours. After a process of adjusting with food grade silicon dioxide, mixing well, centrifuging to remove the upper liquid and air drying in a 40°C oven, the total content of viable sporulated TLRI 211-1 bacteria used in this experiment was 1×10⁹ CFU/g. Commercial B. amyloliquefaciens (CML. B. amyloliquefaciens) used in the treatment 4 was provided by Yungstrong, Vetnostrum Animal Health Co., Ltd, (Hsinchu, Taiwan) and adjusted to the content of 1×10⁹ CFU/g.

After a standardized warm-up, the shuttle repeated sprint ability test involved six repetitions of 2 × 20 m shuttle sprints (approximately 7 s running time). For both tests, sprints were repeated every 20 s (23 (link)). A brisk walk back to the starting line allowed active recovery. Three seconds before starting each sprint, subjects took an individually chosen starting position 0.5 m behind the timing gate. A digital timer started automatically when the player passed the gate. Two timing-gates (Microgate Srl; Race time 2. Light Radio, Bolzano, Italy) working in opposite directions allowed subjects to start the next run from the end where they had finished the preceding sprint. Strong verbal encouragement was provided throughout, and subjects were instructed to perform each sprint with maximal effort. Four scores were calculated: best sprint time, mean sprint time, total sprint time and fatigue index, the last calculated as the percentage decrement: 100–(total time/ideal time × 100); where the ideal time = 6 × best time during test (23 (link)).

The Ag and Zn cytotoxicity was tested using eluates from the two coatings, selecting the concentration corresponding to the highest ion release (i.e., 7 days). Zn/Ag coated plates were kept in medium (D-MEM, Gibco) at 37° C in a humidified 5% CO₂ atmosphere on a plate-shaker (60 rpm). After 7 days the eluates were collected.
The cytotoxicity was evaluated by methylthiazolydiphenyl-tetrazolium bromide (MTT) assay. The assay is a colorimetric test based on the ability of viable cells to convert MTT solution in water-insoluble MTT formazan by mitochondrial dehydrogenases.
Briefly, 2x10³ cells/well were plated in flat-bottomed 96 microplate wells (Costar, Cambridge, MA) in complete D-MEM. After cell adhesion, Ag and Zn eluates were added to culture medium and kept in contact with the cells for 24 h. Cells treated with medium conditioned with Ti alloy plates without Ag/Zn coatings were used as controls. After 24 h, cells were exposed to 5 mg/mL of MTT in complete medium for 4 h at 37 °C, washed with PBS, and 100 μL DMSO was added to each well to dissolve the MTT-formazan crystals. The absorbance was measured at 570 nm by a plate reader spectrophotometer (Tecan Infinite F200pro, Mannedorf, Switzerland). Results were recorded as optical density units (OD₅₇₀) and averaged after blank subtraction. Data were expressed as percentage between OD measured in samples exposed to eluate and OD measured in negative control. The experiment was repeated two times in quadruplicate.