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PubMed1996

Secondary transporters for citrate and the Mg(2+)-citrate complex in Bacillus subtilis are homologous proteins.

Citrate uptake in Bacillus subtilis is mediated by a secondary transporter that transports the complex of citrate and divalent metal ions. The gene coding for the transporter termed CitM was cloned, sequenced, and functionally expressed in Escherichia coli. Translation of the base sequence to the primary sequence revealed a transporter that is not homologous to any known secondary transporter. However, CitM shares 60% sequence identity with the gene product of open reading frame N15CR that is on the genome of B. subtilis and for which no function is known. The hydropathy profiles of the primary sequences of CitM and the unknown gene product are very similar, and secondary structure prediction algorithms predict 12 transmembrane-spanning segments for both proteins. Open reading frame N15CR was cloned and expressed in E. coli and was shown to be a citrate transporter as well. The transporter is termed CitH. A remarkable difference between the two transporters is that citrate uptake by CitM is stimulated by the presence of Mg2+ ions, while citrate uptake by CitH is inhibited by Mg2+. It is concluded that the substrate of CitM is the Mg(2+)-citrate complex and that CitH transports the free citrate anion. Uptake experiments in right-side-out membrane vesicles derived from E. coli cells expressing either CitM or CitH showed that both transporters catalyze electrogenic proton/substrate symport.

Bacillus subtilis↗
PubMed2002

Evaluation of computational metabolic-pathway predictions for Helicobacter pylori.

MOTIVATION: We seek to determine the accuracy of computational methods for predicting metabolic pathways in sequenced genomes, and to understand the contributions of both the prediction algorithms, and the reference pathway databases used by those algorithms, to the prediction accuracy. RESULTS: The comparisons we performed were as follows. (1) We compared two predictions of the pathway complements of Helicobacter pylori that were computed by an early version of our pathway-prediction algorithm: prediction A used the EcoCyc E. coli pathway DB as the reference database (DB) for prediction, and prediction B used the MetaCyc pathway DB (a superset of EcoCyc) as the reference pathway DB. The MetaCyc-based prediction contained 75% more pathway predictions, but we believe a significant number of those predictions were false positives. (2) We compared two predictions of the pathway complement of H. pylori that used MetaCyc as the reference pathway DB, but that used different algorithms: the original PathoLogic algorithm, and an enhanced version of the algorithm designed to eliminate false-positive pathway predictions. The improved algorithm predicted 30\% fewer metabolic pathways than the original algorithm; all of the eliminated pathways are believed to be false-positive predictions. (3) We compared the 98 pathways predicted by the enhanced algorithm with the results of a manual analysis of the pathways of H. pylori. Results: 40 of the computationally predicted pathways were consistent with the manual analysis, 13 pathways are considered false-positive predictions, and four pathways had partially overlapping topologies. Twenty-six predicted pathways were not mentioned in the manual analysis; we believe these are correct predictions by PathoLogic that were not found by the manual analysis. Five pathways from the manual analysis were not found computationally. Agreement between the computational and manual predictions was good overall, with the computational analysis inferring many pathways that the manual analysis did not identify. Ultimately the manual analysis is also partially speculative, and therefore is not an absolute measure of correctness. The algorithm is designed to err on the side of more false positives to bring more potential pathways to the user's attention. The resulting H. pylori pathway DB is freely available at http://ecocyc.org:1555/HPY/organism-summary?object=HPY. AVAILABILITY: The Pathway Tools software is freely available to academic users, and is available to commercial users for a fee. Contact pkarp@ai.sri.com for information on obtaining the software.

Algorithms↗
PubMed2005

A simplified predictive control algorithm for disturbance rejection.

Model predictive control (MPC) offers several advantages for control of chemical processes. However, the standard MPC may do a poor job in suppressing the effects of certain disturbances. This shortcoming is mainly due to the assumption that disturbances remain constant over the prediction horizon. In this paper, a simple disturbance predictor (SDP) is developed to provide predictions of the unmodeled deterministic disturbances for a simplified MPC algorithm. The prediction is developed by curve fitting of the past information. A tuning parameter is employed to handle a variety of disturbance dynamics and a procedure is presented to find an optimum value of the tuning parameter online. A comparison is made with the commonly used disturbance prediction on three example problems. The results show that an improved regulatory performance and zero offset can be achieved under both regular and ramp output disturbances by using the proposed disturbance predictor.

Journal Article↗
PubMedDate not supplied

Prediction of surface and interior regions in proteins--Part II: Predicting secondary structure in regions bound by surface exposed regions.

A new secondary structure prediction algorithm (SecondaryPlot) is described and has been developed for the IBM or compatible computer. This algorithm successfully predicts the boundaries of secondary structural regions by combining hydrophilicity, accessibility and flexibility parameters. Within the limits of these predicted structural regions, five algorithms (Chou and Fasman; Garnier, Osguthorpe and Robson; Delange and Roux; Barkovsky and Bandarin; and Lim) were used to predict helix and sheet regions. All of the algorithms correctly predicted 50-60% of the x-ray defined regions. Predictions were improved by combining the two most successful algorithms of Lim and Chou and Fasman. By combining the boundary predictions (hydrophilicity, accessibility, flexibility and linear polar tripeptide sequences) with secondary structure predictions (Lim, Chou and Fasman), approximately 80% of the x-ray defined structural regions were predicted correctly for 30 proteins (86% for the alpha-class, 79% for the beta-class and 82% for the alpha/beta class of proteins). In our hands, the best of the five algorithms studied to predict the state (helix or sheet) of these structural regions predicted 62% of helical regions and 64% of extended regions in a test set of fifteen proteins. However, SecondaryPlot predicted 78% of helical regions and 71% of extended regions for the same test. SecondaryPlot provides a significant improvement to structure prediction and is a necessary first step in the tertiary structure prediction of proteins.

Algorithms↗
PubMed1996

A segment-based dynamic programming algorithm for predicting gene structure.

An algorithm called segment-based dynamic programming is described for predicting gene structure from a sequence of genomic DNA. The algorithm explores the space of gene structures that satisfy junctional and frame constraints and finds the gene structure that optimizes the sum of junctional and segmental scoring functions. Junctional constraints specify acceptable sites of initiation, termination, and splicing, whereas frame constraints ensure that the total exon length is a multiple of three and that no in-frame stop codons occur within exons or at exon-exon junctions. By computing over segments, segment-based dynamic programming maintains reading frame and phase information for each segment, it can assemble exons in-frame as well as score them in-frame. The algorithm is used to quantify the computational power of constraints. Experimental results show that frame constraints reduce the size of the search space by several orders of magnitude and that cardinality constraints place an asymptotic limit on the size of the search space. The algorithm is also used to compare the accuracy of different methods for assembly and scoring. A scoring scheme based on fifth-order Markov hexamer frequencies is presented and used in three objective functions, corresponding to in-frame, frame-independent, and frame-maximal scoring strategies. Experimental results show that in-frame assembly improves specificity only slightly over frame-independent assembly, whereas in-frame scoring improves specificity substantially over frame-independent and frame-maximal scoring.

Algorithms↗
PubMed2005

Long-term prospective on-line real-time seizure prediction.

OBJECTIVE: Epilepsy, one of the most common neurological disorders, constitutes a unique opportunity to study the dynamics of spatiotemporal state transitions in real, complex, nonlinear dynamical systems. In this study, we evaluate the performance of a prospective on-line real-time seizure prediction algorithm in two patients from a common database. METHODS: We previously demonstrated that measures of chaos and angular frequency, estimated from electroencephalographic (EEG) signals recorded at critical sites in the cerebral cortex, progressively converge (i.e. become dynamically entrained) as the epileptic brain transits from the asymptomatic interictal state to the ictal state (seizure) (Iasemidis et al., 2001, 2002a, 2003a). This observation suggested the possibility of developing algorithms to predict seizures well ahead of their occurrences. One of the central points in those investigations was the application of optimization theory, specifically quadratic zero-one programming, for the selection of the critical cortical sites. This current study combines that observation with a dynamical entrainment detection method to prospectively predict epileptic seizures. The algorithm was tested in two patients with long-term (107.54h) and multi-seizure EEG data B and C (Lehnertz and Litt, 2004). RESULTS: Analysis from the 2 test patients resulted in the prediction of up to 91.3% of the impending 23 seizures, about 89+/-15min prior to seizure onset, with an average false warning rate of one every 8.27h and an allowable prediction horizon of 3h. CONCLUSIONS: The algorithm provides warning of impending seizures prospectively and in real time, that is, it constitutes an on-line and real-time seizure prediction scheme. SIGNIFICANCE: These results suggest that the proposed seizure prediction algorithm could be used in novel diagnostic and therapeutic applications in epileptic patients.

Brain Mapping↗
PubMed2006

[Analysis of the secreted proteins encoded by genes in genome of filamental fungus (Neurospora crassa)].

The completed genome sequence of filamental fungus Neurospora crassa has completed and 10,082 open reading frames (ORFs) were predicted in whole genome. Among them, 437 proteins were secreted ones based on the combination of prediction algorithms SignalP v3.0 and PSORT, transmembrane domains prediction algorithms TMHMM v2.0 and THUMBUP, potential GPI-anchor sites prediction algorithm big-PI predictor and the subcellular protein location prediction algorithm TargetP v1.01. The minimum ORF length of the encoding sequences for 437 secreted proteins was 252 bp, the maximum ORF length was 6 604 bp and average was 1 433 bp. The length of signal peptide of secreted proteins ranged from 15 to 59 amino acids. Among the 437 secreted proteins, 205 were provided with function description. The function of secretory proteins were involved in kinds of enzyme, cell energy, transmission, cell recovered itself and defense mechanism et al. These results implied that processes catalyzed by these proteins probably occurred outside of cells, at least partially.

Amino Acid Sequence↗
PubMed2004

An algorithm to predict implantable cardioverter-defibrillator lead failure.

OBJECTIVES: The goal of this analysis was to test an algorithm that identifies implantable cardioverter-defibrillator (ICD) lead problems before clinical failure and/or inappropriate therapy. BACKGROUND: The ICD lead failures typically present as inappropriate shock therapy. Identifying lead failures before their clinical presentation may prevent patient discomfort, improve device longevity, and avoid device-induced proarrhythmia. METHODS: We tested an algorithm that uses two measures of oversensing and one measure of abnormal impedance to detect a lead failure. The oversensing measures consisted of a counter for RR intervals <140 ms and nonsustained ventricular tachycardia episodes with mean RR interval <200 ms. The impedance measure tracked lead impedances every day and each week. Abnormal impedance was defined as a decrease in impedances or an outlier value compared with baseline. Lead failures were identified when both oversensing measures were met or abnormal impedance and one oversensing measure occurred. The stored data from 696 patients with an ICD were analyzed to determine the sensitivity and specificity of the algorithm to detect lead failures. RESULTS: Twenty-nine patients demonstrated clinical lead failures with an average of 6 +/- 9 inappropriate shocks per patient. The two oversensing measures used in the algorithm predicted 72% (21 of 29) of the lead failures. Fulfilling at least two of the three impedance and oversensing measures, the sensitivity of our algorithm was 83% (24 of 29) with a 100% (667 of 667) specificity. CONCLUSION: Oversensing combined with abnormal impedance trends may be used to identify ICD lead failures with high sensitivity and very high specificity.

Algorithms↗
PubMed1997

[Simulation of a decoupling generalized predictive control algorithm for temperature-humidity system].

A decoupling generalized predictive control algorithm was presented in this paper. This algorithm was proposed to control the temperature-humidity system of a practical air-conditioning environmental chamber. By introducing absolute humidity as an intermediate variable, temperature and humidity of the system can be decoupled. Then the two decoupled systems are controlled by generalized predictive control (GPC) algorithm at different sample rates. Many simulation experiments demonstrated the effectiveness of the algorithm. Besides, decoupling of temperature and humidity will be extremely helpful to the application of GPC on the real system. This is also the effective method to control the temperature and humidity system.

Air Conditioning↗
PubMed1996

A mechanistic algorithm for predicting blood:air partition coefficients of organic chemicals with the consideration of reversible binding in hemoglobin.

The objectives of the present study were (i) to develop a mechanistic algorithm for predicting blood:air partition coefficients (PCs) of volatile organic chemicals (VOCs), and (ii) to apply this algorithm to predict the rat blood:air PCs of several VOCs. The approach consisted initially of developing an algorithm to predict the blood:air PCs of VOCs solely based on the solubility phenomenon and then of extending the algorithm to include protein binding. The algorithm based on solubility phenomenon predicted blood:air PCs by dividing the estimated solubility of chemicals in blood by their saturable vapor concentrations at 37 degrees C. The rat blood:air PCs predicted using this algorithm were in close agreement with the experimental values for relatively hydrophilic VOCs such as ketones, alcohols, acetate esters, and diethyl ether (with an average ratio of 0.80 between predicted and experimental values), whereas there was a marked discrepancy in the case of relatively lipophilic VOCs such as alkanes, haloalkanes, and aromatic hydrocarbons (with an average ratio of 0.21 between predicted and experimental values). This discrepancy was hypothesized to be due to the occurrence of reversible binding of these substances in rat hemoglobin based on literature evidence of the existence of hydrophobic holes (or "xenon-binding" pockets). The association constants (Ka) for the presumed reversible hemoglobin binding of several alkanes, haloalkanes, and aromatic hydrocarbons were estimated from the difference between chemical concentration in rat erythrocytes predicted by the solubility-based algorithm and that deduced from the previously published experimental blood:air PCs for these chemicals (which presumably included contribution of hemoglobin binding in addition to "true" solubility). The Ka values estimated in this manner ranged from 504 to 4725 M-1 for the chemicals investigated in the present study. The a priori predictions of the percentage of several VOCs (diethyl ether, methyl isobutyl ketone, n-hexane, toluene, and chloroform) in rat erythrocytes obtained with the algorithm using these Ka estimates corresponded well with previously published experimental data. The mechanistic algorithm developed in the present study should be useful for predicting the "apparent" blood:air PCs of VOCs regardless of exposure concentrations, by accounting for the relative contributions of both the true chemical solubility and reversible hemoglobin binding.

Alcohols↗
PubMed1996

A tissue composition-based algorithm for predicting tissue:air partition coefficients of organic chemicals.

The objectives of the present study were (i) to develop an algorithm for predicting the tissue:air partition coefficients (PCs) of volatile organic chemicals (VOCs) and (ii) to apply this algorithm to predict the rat tissue:air PCs of 45 VOCs. The approach consisted of estimating the tissue:air PCs by dividing the tissue solubility of chemicals by their saturable vapor concentrations. The tissue solubility of chemicals was calculated as the sum total of their solubility in neutral lipid, phospholipid, and water fractions of tissues. The rat liver:air, muscle:air, and adipose tissue:air PCs predicted using this algorithm compared well with literature data available for several ketones, alcohols, acetate esters, alkanes, haloalkanes, aromatic hydrocarbons, and diethyl ether. The average ratios between the predicted and experimental values of the tissue:air PC values were 0.94 (liver), 0.93 (muscle), and 1.10 (adipose tissue). The mechanistic algorithm developed in the present study should be useful for predicting tissue:air PCs of VOCs and for verifying the current default assumption of considering tissue:air PCs to be species-invariant.

Adipose Tissue↗
PubMed2004

Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure.

A dynamic programming algorithm for prediction of RNA secondary structure has been revised to accommodate folding constraints determined by chemical modification and to include free energy increments for coaxial stacking of helices when they are either adjacent or separated by a single mismatch. Furthermore, free energy parameters are revised to account for recent experimental results for terminal mismatches and hairpin, bulge, internal, and multibranch loops. To demonstrate the applicability of this method, in vivo modification was performed on 5S rRNA in both Escherichia coli and Candida albicans with 1-cyclohexyl-3-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate, dimethyl sulfate, and kethoxal. The percentage of known base pairs in the predicted structure increased from 26.3% to 86.8% for the E. coli sequence by using modification constraints. For C. albicans, the accuracy remained 87.5% both with and without modification data. On average, for these sequences and a set of 14 sequences with known secondary structure and chemical modification data taken from the literature, accuracy improves from 67% to 76%. This enhancement primarily reflects improvement for three sequences that are predicted with <40% accuracy on the basis of energetics alone. For these sequences, inclusion of chemical modification constraints improves the average accuracy from 28% to 78%. For the 11 sequences with <6% pseudoknotted base pairs, structures predicted with constraints from chemical modification contain on average 84% of known canonical base pairs.

Algorithms↗
PubMed1991

Algorithm to predict triple-vessel/left main coronary artery disease in patients without myocardial infarction. An international cross validation.

Logistic regression was applied to the clinical, risk factor, and exercise data of consecutive angiographic referrals without prior myocardial infarction to determine an algorithm predicting the probability of triple-vessel/left main coronary artery disease. These data were obtained from a total of 1,074 such subjects from patient populations at four centers (Cleveland Clinic Foundation, Cleveland, Ohio; Hungarian Institute of Cardiology, Budapest, Hungary; the university hospitals, Zurich and Basel, Switzerland; and the Veterans Administration Medical Center, Long Beach, Calif.) and used to derive four separate probability algorithms. Each algorithm is based on patient data from study samples at three of the four centers and consists of 272 logistic functions, which are related to linear combinations of 13 variables (age, sex, type of chest pain, systolic blood pressure, resting electrocardiogram, serum cholesterol, fasting blood sugar, achieved exercise work load, achieved heart rate, exercise-induced angina and hypotension, heart rate-adjusted resting ST depression, and exercise ST slope). The four algorithms were cross validated by testing them on the populations not involved in their derivation. The resulting probabilities in the four test groups were then compared with the angiographic findings of triple-vessel/left main coronary artery disease. The discriminatory power of all the algorithms was fair to good (area under receiver operating characteristic curve, 0.68, 0.75, 0.82, 0.85) in the test groups. The algorithm did not significantly underestimate or overestimate disease probability except in one center (Long Beach).(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed2006

PromoterExplorer: an effective promoter identification method based on the AdaBoost algorithm.

MOTIVATION: Promoter prediction is important for the analysis of gene regulations. Although a number of promoter prediction algorithms have been reported in literature, significant improvement in prediction accuracy remains a challenge. In this paper, an effective promoter identification algorithm, which is called PromoterExplorer, is proposed. In our approach, we analyze the different roles of various features, that is, local distribution of pentamers, positional CpG island features and digitized DNA sequence, and then combine them to build a high-dimensional input vector. A cascade AdaBoost-based learning procedure is adopted to select the most 'informative' or 'discriminating' features to build a sequence of weak classifiers, which are combined to form a strong classifier so as to achieve a better performance. The cascade structure used for identification can also reduce the false positive. RESULTS: PromoterExplorer is tested based on large-scale DNA sequences from different databases, including the EPD, DBTSS, GenBank and human chromosome 22. Experimental results show that consistent and promising performance can be achieved.

Algorithms↗
PubMed2026

Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.

OBJECTIVE: To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. METHODS: For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or non-peer-reviewed papers were excluded.Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two reviewers screened titles and abstracts independently, then populated a purpose-built data extraction form. A subsequent qualitative thematic analysis focused on algorithms' strengths, added value, potential harms, unintended consequences and equity implications.The main outcome assessed was whether, among healthy adults, the algorithm improved CVD risk prediction relative to the FRS. RESULTS: Of 707 studies retrieved, 29 met inclusion criteria. 23 reported improved predictive ability relative to the FRS. Most datasets and/or medical records used included sociodemographic predictors of CVD not included among FRS inputs. Some added costly diagnostic tests like CT angiography to FRS screening indicators. When they were defined, inputs and outcomes such as hypertension or myocardial infarction did not always adhere to FRS values. Statistical significance was generally taken as a proxy for clinical significance. Some algorithms overestimated the number at risk compared with the FRS without discussing whether that larger proportion might be at risk of overdiagnosis rather than CVD, while a few decreased the proportion found to be at risk. CONCLUSIONS: Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these. Still needed to achieve patient benefit is clinical insight, adherence to screening principles and cost-benefit assessment of inputs selected.

Humans↗
PubMed1996

A simple algorithm to predict the development of radiological erosions in patients with early rheumatoid arthritis: prospective cohort study.

OBJECTIVE: To produce a practical algorithm to predict which patients with early rheumatoid arthritis will develop radiological erosions. DESIGN: Primary care based prospective cohort study. SETTING: All general practices in the Norwich Health Authority, Norfolk. SUBJECTS: 175 patients notified to the Norfolk Arthritis Register were visited by a metrologist soon after they had presented to their general practitioners with inflammatory polyarthritis, and again after a further 12 months. All the patients satisfied the American Rheumatism Association's 1987 criteria for rheumatoid arthritis and were seen by a metrologist within six months of the onset of symptoms. The study population was randomly split into a prediction sample (n = 105) for generating the algorithm and a validation sample (n = 70) for testing it. MAIN OUTCOME MEASURES: Predictor variables measured at baseline included rheumatoid factor status, swelling of specific joint areas, duration of morning stiffness, nodules, disability score, age, sex, and disease duration when the patient first presented. The outcome variable was the presence of radiological erosions in the hands or feet, or both, after 12 months. RESULTS: A simple algorithm based on a combination of three variables--a positive rheumatoid factor test, swelling of at least two large joints, and a disease duration of more than three months--was best able to predict erosions. When the accuracy of this algorithm was tested with the validation sample, the erosion status of 79% of patients was predicted correctly. CONCLUSIONS: A simple algorithm based on three easily measured items of information can predict which patients are at high risk and which are at low risk of developing radiological erosions.

Adult↗
PubMed2001

CODA: a combined algorithm for predicting the structurally variable regions of protein models.

CODA, an algorithm for predicting the variable regions in proteins, combines FREAD a knowledge based approach, and PETRA, which constructs the region ab initio. FREAD selects from a database of protein structure fragments with environmentally constrained substitution tables and other rule-based filters. FREAD was parameterized and tested on over 3000 loops. The average root mean square deviation ranged from 0.78 A for three residue loops to 3.5 A for eight residue loops on a nonhomologous test set. CODA clusters the predictions from the two independent programs and makes a consensus prediction that must pass a set of rule-based filters. CODA was parameterized and tested on two unrelated separate sets of structures that were nonhomologous to one another and those found in the FREAD database. The average root mean square deviation in the test set ranged from 0.76 A for three residue loops to 3.09 A for eight residue loops. CODA shows a general improvement in loop prediction over PETRA and FREAD individually. The improvement is far more marked for lengths six and upward, probably as the predictive power of PETRA becomes more important. CODA was further tested on several model structures to determine its applicability to the modeling situation. A web server of CODA is available at http://www-cryst.bioc.cam.ac.uk/~charlotte/Coda/search_coda.html.

Algorithms↗
PubMed1995

An algorithm for predicting tissue: blood partition coefficients of organic chemicals from n-octanol: water partition coefficient data.

The objectives of the present study were (1) to develop an algorithm to predict tissue:blood partition coefficients (PCs) of organic chemicals from n-octanol: water (Ko/w) PC data, and (2) to apply this algorithm to predict the rat tissue:blood PCs of some relatively hydrophilic organics, particularly ketones, alcohols, and acetate esters. The algorithm, developed by modifying a previously published one, involved predicting tissue:blood PCs of chemicals by dividing their partitioning into tissues by the sum of their partitioning into erythrocytes and plasma. The partitioning of a chemical into tissues, erythrocytes, and plasma was expressed as an additive function of its partitioning into neutral lipids, phospholipids, and water contained in them. The muscle, liver, and adipose tissue:blood PCs predicted with the present method were compared with the experimental values obtained from the literature for five ketones, eight alcohols, and eight acetate esters. The predicted muscle:blood and liver:blood PCs for the set of 21 hydrophilic organics were within a factor of 1.01 and 0.99 (on an average), respectively, of the experimental values. However, the predicted adipose tissue:blood PCs of the hydrophilic organics were greater than the experimental values by a factor of 4.13, which improved when vegetable oil:saline (Ko/s) PCs were used instead of Ko/w PCs (factor of 1.51). Overall, the use of the present algorithm should enable the prediction of tissue:blood PCs for organic chemicals for which Ko/w or Ko/s data are available.

Acetates↗