Ratio Beta Download |LINK|
Roseanna Diomede
Multi-pass testing uses a specified contaminate, of known sizes, added regularly in measured quantities to the fluid which is pumped continuously through the filter. Measured samples of the fluid are then taken at timed intervals from both the downstream and the upstream of the filter simultaneously, particles are measured and counted by electronic means using automatic particles counters.
From these measurements a Beta ratio (b) is formulated by dividing the number of particles of a particular size in the upstream flow by the number of particles of the same size in the downstream flow:
where bx is the beta ratio for contaminant larger than x mm
Nu is the number of particles larger than x mm per unit of volume upstream
Nd is the number of particles larger than x mm per unit of volume downstream.
The beta ratio is an indicator of how well a filter controls particulate: i.e., if one out of every two particles (>xmm) in the fluid pass through the filter, the beta ratio at xmm is 2, if one out of every 200 of the particles (>xmm) pass through the filter the beta ratio is 200.
Therefore, filters with a higher beta ratio retain more particles and have higher efficiency.
Efficiency for a given particle size (Ex) can be derived directly from the beta ratio by the following equation:
Beta Ratio equals the ratio of the number of particles of a minimum given size upstream of the filter to the number of particles of the same size and larger found downstream. Simply put, the higher the Beta Ratio the higher the Capture Efficiency of the filter.
Filter ratings are an often-misunderstood area of contamination control. The most commonly used rating is the Beta Ratio, which is defined as the ratio of the number of particles upstream of the test filter versus the number downstream, greater than a given size. Using the Beta Ratio, a 5 micron filter with a Beta 1000 Rating, will have on average 1000 particles larger than 5 micron upstream of the filter for everyone 5 micron or greater particle downstream.
The efficiency of the filter can be calculated directly from the Beta Ratio since the % efficiency is simply (beta-1)/beta x 100. A Beta 1000, 5-micron filter is thus said to be 99.99% efficient at removing 5 micron and larger particles.
Measured samples of the fluid are then taken at timed intervals from both the downstream and the upstream of the filter at the same time, particles are measured and counted by electronic means using automatic particles counters. From these measurements a Beta ratio (b) is devised by dividing the number of particles of a particular size in the upstream flow by the number
The beta ratio is a sign of how well a filter controls particulate: for example, if one out of every two particles (> x mm) in the fluid pass through the filter, the beta ratio at x mm is 2, if one out of every 200 of the particles (> x mm) pass through the filter the beta ratio is 200. Thus, filters with a higher beta ratio hold more particles and have higher efficiency. Efficiency for a given particle size (Ex) can be derived directly from the beta ratio by the following equation:
I am wondering about the use of odds ratio (OR) versus the Beta-coefficient of each SNP variant in a risk score model. For instance, here they used the Beta-coefficient in their model, while here they used the odds ratio. Is there any difference in using the odds ratio versus the Beta-coefficient in a risk score model? Also, I noticed that some papers use log(OR) rather than ln(OR), is there a major difference between both?
Here, the beta coefficient for gene1 in relation to condition B versus A is 0.07294. So, gene1 increases in expression in condition B (if it decreased, the beta coefficient would be negative). This is not a statistically significant finding, though, with p=0.887.
I'm a student and do research in this area and after a lot of reading, I'm pretty sure you want to use the log odds (Betas) as your weights for your model. The beta is the true weighting even though the OR is more often reported, I believe because it is easier for humans to understand. Also, a lot of the time people say Log(OR) they mean LN(OR). In this field, or generally in bioinformatics, it appears that LN is the default type of logarithmic transformation, so unless you see someone write Log10, they probably mean LN.
To calculate the beta of a security, the covariance between the return of the security and the return of the market must be known as well as the variance of the market returns. The covariance of the return of an asset with the return of the benchmark is divided by the variance of the return of the benchmark over a certain period.
The use of biomarkers has strengthened the link between clinical dementia and AD pathophysiology [2]. Currently, cerebrospinal fluid (CSF) biomarkers are commonly used in specialized memory clinics [4]. A characteristic feature of AD progression is a reduction in amyloid-β (Aβ) protein (that is, low CSF Aβ42 level) and an increase of neuronal degeneration biomarkers (that is, increase of CSF total tau and phosphorylated tau (p-tau181) levels) in CSF of subjects with AD [5]. The decrease in CSF Aβ42 levels appears to be an early phenomenon in AD progression and is evident over two decades prior to the first clinical sign [6]. Unfortunately, because CSF Aβ42 levels can also be low in non-AD patients, this biomarker alone is of limited use in a clinical setting [7,8]. The limited use of CSF biomarkers can lead to indeterminate results, revealing abnormal tau protein values and normal Aβ peptide levels or the inverse [2]. Furthermore, CSF biomarker results are also characterized by a large intersite variability [7] that requires the use of internally validated cutoff levels for each laboratory [9].
Biological interpretation of cerebrospinal fluid results according to different methods. Method 1: Cerebrospinal fluid (CSF) phosphorylated tau (p-tau181) and CSF amyloid-β42 (Aβ42). Method 2: CSF p-tau181 and Aβ 42/40 ratio. Method 3: First measure p-tau181 and CSF Aβ42, then use of Aβ 42/40 ratio instead of Aβ42 in case of discrepancy. Proportions of indeterminate profiles according to the methods were compared using the McNemar test.
The Aβ 42/40 ratio was the only biomarker that was consistent between centers in AD and non-AD patients. We may therefore hypothesize that this ratio is less sensitive to preanalytical and analytical conditions.
Our findings confirm and strengthen the results of a recent study in which researchers reported that the use of the Aβ 42/40 ratio may contribute to decreases in the proportion of indeterminate CSF profiles in the clinical setting [16].
The proportion of patients with AD varied greatly at the three centers, reflecting the differences in recruitment and practices of memory clinics. Despite significant intercenter differences in reporting CSF Aβ40 and CSF Aβ42 levels, the mean Aβ 42/40 ratios were comparable across the three centers, ranging from 0.044 to 0.049 in patients with AD. We therefore hypothesize that the use of the CSF amyloid ratio could contribute to decreased preanalytical and analytical sources of variability among centers. Interestingly, we found that optimal cutoff for the ratio in the overall study population was equal to 0.055, which was comparable to the 0.057 cutoff recently reported by another team in a monocentric study [12].
Very few data are available concerning indeterminate profiles of CSF biomarkers. In our study population, on the basis of routine clinical practice, this situation was not rare, being observed in 22% of the patients even after optimization procedures using local optimum cutoffs. We have shown that Aβ 42/40 ratio instead of Aβ42 leads to a clear biological conclusion in more than 50% of these indeterminate cases. Reclassification analyses also showed that this approach is more congruent with the diagnosis by clinicians.
This study has several strengths, including its large size, its multicentric and prospective design, and the use of a common CSF polypropylene tubing at each of the three centers to standardize CSF evaluation. In addition, we used a NRI method that compares different strategies of biomarker analyses and is more precise than traditional analyses based on ROC curves. The main limitation of the results is the lack of neuropathological validations. Also, clinicians were not blinded to CSF results prior to clinical diagnosis, which may generate a circular reasoning bias in our findings. The absence of a centralized measurement for CSF biomarkers is another limitation, owing to the persistence of an intersite variability despite the use of a common tubing to collect CSF. Furthermore, the non-AD group was heterogeneous and included patients with cognitive disorders, of both psychiatric and neurologic origins. However, our study was aimed at reflecting the standard practice at memory clinics. The wide variety of patients with a number of diseases reflects the broad spectrum of cognitive complaints referred to memory centers. Finally, this study excluded patients with MCI. Inclusion of these patients in future studies will help to determine the links between the amyloid ratio and the rate of conversion from MCI to AD.
Using a large prospective multicenter cohort of patients with cognitive disorders, we did not find an added value for the systematic assessment of the CSF Aβ 42/40 ratio. However, in cases of discrepancy between CSF p-tau and CSF Aβ42, the use of Aβ 42/40 ratio allowed reaching a biological conclusion in more than 50% of indeterminate results and improved the biological congruence with clinical diagnoses.
JD processed and analyzed the data, prepared the results presentation and drafted various versions of the manuscript. SS participated in the research project (conception, organization, execution) and in manuscript preparation (review and critique). AG participated in the research project (conception, organization, execution) and in manuscript preparation (review and critique). OV participated in the research project (conception, organization, execution) and in manuscript preparation (review and critique). SB participated in the research project (conception, organization, execution) and in manuscript preparation (review and critique). JLL participated in the research project (conception, organization, execution) and in manuscript preparation (review and critique). KP participated in the research project (conception, organization, execution) and in manuscript preparation (review and critique). BS participated in the research project (conception, organization, execution) and in manuscript preparation (review and critique). KVK participated in the research project (interpretation of data, execution) and in manuscript preparation (review and critique). CD participated in the research project (conception, organization, execution) and in manuscript preparation (review and critique). FP participated in the research project (conception, organization, execution) and in manuscript preparation (review and critique). JT participated in the research project (conception, organization, execution) and in manuscript preparation (review and critique). JH participated in the research project (conception, organization, execution) and in manuscript preparation (review and critique) and helped draft the manuscript. CP participated in the research project (conception, organization, execution) and in manuscript preparation (review and critique) and helped draft the manuscript. SL helped with data analysis, participated in the research project (conception, organization, execution) and in manuscript preparation (review and critique). All authors edited and approved the final version of the manuscript.
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