Normal Probability Function

An accurate scoring function is expected to correctly select the most stable structure from a set of pose candidates. One can hypothesize that a scoring function’s ability to identify the most stable structure might be improved by emphasizing the most relevant atom pairwise interactions. However, it is hard to evaluate the relevant importance for each atom pair using traditional means. With the introduction of machine learning methods, it has become possible to determine the relative importance for each atom pair present in a scoring function. In this work, we use the Random Forest (RF) method to refine a pair potential developed by our laboratory (GARF6) by identifying relevant atom pairs that optimize the performance of the potential on our given task. Our goal is to construct a machine learning (ML) model that can accurately differentiate the native ligand binding pose from candidate poses using a potential refined by RF optimization. We successfully constructed RF models on an unbalanced data set with the ‘comparison’ concept and, the resultant RF models were tested on CASF-2013.5 In a comparison of the performance of our RF models against 29 scoring functions, we found our models outperformed the other scoring functions in predicting the native pose. In addition, we used two artificial designed potential models to address the importance of the GARF potential in the RF models: (1) a scrambled probability function set, which was obtained by mixing up atom pairs and probability functions in GARF, and (2) a uniform probability function set, which share the same peak positions with GARF but have fixed peak heights. The results of accuracy comparison from RF models based on the scrambled, uniform, and original GARF potential clearly showed that the peak positions in the GARF potential are important while the well depths are not. <br>

Download Full-text

Potential Use of Aquatic Macrophytes to Enhance the Treatment of Septic Tank Liquids

Water Science & Technology ◽

10.2166/wst.1999.0166 ◽

1999 ◽

Vol 40 (3) ◽

pp. 225-232 ◽

Cited By ~ 2

Author(s):

S. Perdomo ◽

C. Bangueses ◽

J. Fuentes

Keyword(s):

Aquatic Macrophytes ◽

Typha Latifolia ◽

Correlation Coefficients ◽

Statistical Characteristics ◽

Septic Tank ◽

Conventional System ◽

Normal Probability ◽

Complete Treatment ◽

Floating Macrophyte ◽

Potential Use

In several urban and suburban areas, the problem of the disposal and treatment of septic tank liquids has not been solved yet. This paper deals with the primary operational evaluation of a conventional system of ponds used at Tarariras, in the Department of Colonia, Uruguay, as well as the potential use of aquatic macrophytes to enhance such treatment. The conventional system was sampled over a period of approximately one month at the end of the summer in order to determine the main parameters. Groups of up to 20 samples were studied to determine the normal distributions. Correlation coefficients were obtained for the normal probability plot between 0.84 and 0.99. The most relevant statistical characteristics were calculated for each parameter. The removal efficiency was 80.0% of BOD5, 58.5% of COD, 75.8% of NH4+-N, 9.5% of PO4−3-P and 38.5% of TSS. At the same time, batch and semi-continuous trials were carried out at bench scale with Eichhornia crassipes (floating macrophyte) and Typha latifolia (emergent macrophyte). The best efficiencies were obtained for the latter, with values of 96.6% of BOD5, 93.0% of COD, 99.6% of NH4+-N, 95.2% of PO4−3-P and 95.5% of TSS. It was concluded that constructed wetlands could be the answer to a more complete treatment process.

Download Full-text

Integration of various scales for measurement of insomnia

Research Methods in Medicine & Health Sciences ◽

10.1177/26320843211010044 ◽

2021 ◽

pp. 263208432110100

Author(s):

Satyendra Nath Chakrabartty

Keyword(s):

Test Scores ◽

Data Driven ◽

Weighted Sum ◽

Future Studies ◽

Normal Probability ◽

Equivalent Test ◽

Item Scores ◽

Using Data ◽

Set Up ◽

Different Levels

Background Scales for evaluating insomnia differ in number of items, response format, and result in different scores distributions and score ranges and may not facilitate meaningful comparisons. Objectives Transform ordinal item-scores of three scales of insomnia to continuous, equidistant, monotonic, normally distributed scores, avoiding limitations of summative scoring of Likert scales. Methods Equidistant item-scores by weighted sum using data-driven weights to different levels of different items, considering cell frequencies of Item-Levels matrix, followed by normalization and conversion to [1, 10]. Equivalent test-scores (as sum of transformed item- scores) for a pair of scales were found by Normal Probability curves. Empirical illustration given. Results Transformed test-scores are continuous, monotonic and followed Normal distribution with no outliers and tied scores. Such test-scores facilitate ranking, better classification and meaningful comparison of scales of different lengths and formats and finding equivalent score combinations of two scales. For a given value of transformed test-score of a scale, easy alternate method avoiding integration proposed to find equivalent scores of another scales. Equivalent scores of scales help to relate various cut-off scores of different scales and uniformity in interpretations. Integration of various scales of insomnia is achieved by finding one-to-one correspondence among the equivalent score of various scales with correlation over 0.99 Conclusion Resultant test-scores facilitated undertaking analysis in parametric set up. Considering the theoretical advantages including meaningfulness of operations, better comparison, use of such method of transforming scores of Likert items/test is recommended test and items, Future studies were suggested.

Download Full-text