First-order Dependencies Research Articles

Kinetics and Mechanism of the Comproportionation of Hypothiocyanous Acid and Thiocyanate to Give Thiocyanogen in Acidic Aqueous Solution

The kinetics of comproportionation of hypothiocyanous acid (HOSCN) and thiocyanate (SCN-) to give thiocyanogen ((SCN)2) in acidic aqueous solutions have been determined by double-mixing stopped-flow UV spectroscopy. Hypothiocyanite (OSCN-) was generated at pH 13 by oxidation of excess SCN- with hypobromite (OBr-), followed by a pH jump to acidic conditions ([H+] = 0.20-0.46 M). The observed pseudo-first-order rate constants exhibit first-order dependencies on [H+] and [SCN-] with overall third-order kinetics. The corresponding kinetics of hydrolysis of (SCN)2 have also been examined. Under conditions of high (and constant) [H+] and [SCN-], the kinetics exhibit second-order behavior with respect to [(SCN)2] and complex inverse dependences on [H+] and [SCN-]. Under conditions of low [H+] and [SCN-], the kinetics exhibit first-order behavior with respect to [(SCN)2] and independence with respect to [H+] and [SCN-]. We attribute this behavior to a shift in the rate-limiting step from disproportionation of HOSCN (second-order dependency on [(SCN)2]) to rate-limiting hydrolysis (first-order dependency on [(SCN)2]). Thus, we have determined the following equilibrium constant by the kinetic method: (SCN)2 + H2O HOSCN + SCN- + H+; Khyd = [HOSCN][SCN-][H+]/[(SCN)2] = khyd/kcomp = 19.8(+/-0.7) s-1/ 5.14(+/-0.07) x 103 M-2 s-1 = 3.9 x 10-3 M2.

Low-Order Conditional Independence Graphs for Inferring Genetic Networks

As a powerful tool for analyzing full conditional (in-)dependencies between random variables, graphical models have become increasingly popular to infer genetic networks based on gene expression data. However, full (unconstrained) conditional relationships between random variables can be only estimated accurately if the number of observations is relatively large in comparison to the number of variables, which is usually not fulfilled for high-throughput genomic data. Recently, simplified graphical modeling approaches have been proposed to determine dependencies between gene expression profiles. For sparse graphical models such as genetic networks, it is assumed that the zero- and first-order conditional independencies still reflect reasonably well the full conditional independence structure between variables. Moreover, low-order conditional independencies have the advantage that they can be accurately estimated even when having only a small number of observations. Therefore, using only zero- and first-order conditional dependencies to infer the complete graphical model can be very useful. Here, we analyze the statistical and probabilistic properties of these low-order conditional independence graphs (called 0-1 graphs). We find that for faithful graphical models, the 0-1 graph contains at least all edges of the full conditional independence graph (concentration graph). For simple structures such as Markov trees, the 0-1 graph even coincides with the concentration graph. Furthermore, we present some asymptotic results and we demonstrate in a simulation study that despite their simplicity, 0-1 graphs are generally good estimators of sparse graphical models. Finally, the biological relevance of some applications is summarized.

Mechanistic investigations of the palladium-catalyzed aerobic oxidative kinetic resolution of secondary alcohols using (-)-sparteine.

The mechanistic details of the Pd(II)/(-)-sparteine-catalyzed aerobic oxidative kinetic resolution of secondary alcohols were elucidated, and the origin of asymmetric induction was determined. Saturation kinetics were observed for rate dependence on [(-)-sparteine]. First-order rate dependencies were observed for both the Pd((-)-sparteine)Cl(2) concentration and the alcohol concentration at high and low [(-)-sparteine]. The oxidation rate was inhibited by addition of (-)-sparteine HCl. At low [(-)-sparteine], Pd-alkoxide formation is proposed to be rate limiting, while at high [(-)-sparteine], beta-hydride elimination is proposed to be rate determining. These conclusions are consistent with the measured kinetic isotope effect of k(H)/k(D) = 1.31 +/- 0.04 and a Hammett rho value of -1.41 +/- 0.15 at high [(-)-sparteine]. Calculated activation parameters agree with the change in the rate-limiting step by increasing [(-)-sparteine] with DeltaH(++) = 11.55 +/- 0.65 kcal/mol, DeltaS(++) = -24.5 +/- 2.0 eu at low [(-)-sparteine], and DeltaH(++) = 20.25 +/- 0.89 kcal/mol, DeltaS() = -5.4 +/- 2.7 eu at high [(-)-sparteine]. At high [(-)-sparteine], the selectivity is influenced by both a thermodynamic difference in the stability of the diastereomeric Pd-alkoxides formed and a kinetic beta-hydride elimination to maximize asymmetric induction. At low [(-)-sparteine], the selectivity is influenced by kinetic deprotonation, resulting in lower k(rel) values. A key, nonintuitive discovery is that (-)-sparteine plays a dual role in this oxidative kinetic resolution of secondary alcohols as a chiral ligand on palladium and as an exogenous chiral base.

Modeling the kinetics of ferrous iron oxidation by monochloramine.

The maintenance of disinfectants in distribution systems is necessary to ensure drinking water safety. Reactions with oxidizable species can however lead to undesirable disinfectant losses. Previous work has shown that the presence of Fe(II) can cause monochloramine loss in distribution system waters. This paper further examines these reactions and presents a reaction mechanism and kinetic model. The mechanism includes both aqueous-phase reactions and surface-catalyzed reactions involving the iron oxide product. In addition, it considers competitive reactions involving the amidogen radical that lead to a nonelementary stoichiometry. Using the method of initial rates, the aqueous-phase reactions were found to have first-order dependencies on Fe(II), NH2Cl, and OH- and a rate coefficient (kNH2Cl,soln) of 3.10 (+/-0.560) x 10(9) M(-2) min(-1). The surface-mediated reactions were modeled by assuming the formation of two surface species: >FeOFe+ and >FeOFeOH. Using numerical techniques, combined rate coefficients for the surface-mediated processes were determined to be 0.56 M(-3) min(-1) and 3.5 x 10(-18) M(-4) min(-1), respectively. The model was then used to examine monochloramine and Fe(II) stability under conditions similar to those observed in distribution systems. Our findings suggest the potential utility of monochloramine as an oxidant for Fe(III) removal in drinking water treatment.

The Effects of CO2, CO and H2 Co-Reactants on Methane Reactions Catalyzed by Mo/H-ZSM-5

The effects of CO2, CO and H2 co-reactants on CH4 pyrolysis reactions catalyzed by Mo/H-ZSM-5 were investigated as a function of reaction temperatures and co-reactant and CH4 concentrations. Total CH4 conversion rates were not affected by CO2 co-reactants, except at high CO2 pressures, which led to the oxidation of the active MoCx species, but CHx intermediates formed in rate-determining C–H bond activation steps increasingly formed CO instead of hydrocarbons as CO2 concentrations increased. CO formation rates increased with increasing CO2 partial pressure; all entering CO2 molecules reacted with CH4 within the catalyst bed to form two CO molecules at 950-1033 K. In contrast, hydrocarbon formation rates decreased linearly with increasing CO2 partial pressure and reached undetectable levels at CO2/CH4 ratios above 0.075 at 950 K. CO formation continued for a short period of time at these CO2/CH4 molar ratios, but then all catalytic activity ceased, apparently as a result of the conversion of active carbide structures to MoOx. The removal of CO2 from the CH4 stream led to gradual catalyst reactivation via reduction-carburization processes similar to those observed during the initial activation of MoOx/H-ZSM-5 precursors in CH4. The CO2/CH4 molar ratios required to inhibit hydrocarbon synthesis were independent of CH4 pressure because of the first-order kinetic dependencies of both CH4 and CO2 activation steps. These ratios increased from 0.075 to 0.143 as reaction temperatures increased from 950 to 1033 K. This temperature dependence reflects higher activation energies for reductant (CH4) than for oxidant (CO2) activation, leading to catalyst oxidation at higher relative oxidant concentrations as temperature increases. The scavenging of CHx intermediates by CO2-derived species leads also to lower chain growth probabilities and to a significant inhibition of catalyst deactivation via oligomerization pathways responsible for the formation of highly unsaturated unreactive deposits. CO co-reactants did not influence the rate or selectivity of CH4 pyrolysis reactions on Mo/H-ZSM-5; therefore, CO formed during reactions of CO2/CH4 mixtures are not responsible for the observed effects of CO2 on reaction rates and selectivities, or in catalyst deactivation rates during CH4 reactions. H2 addition studies showed that H2 formed during CH4/CO2 reactions near the bed inlet led to inhibited catalyst deactivation in downstream catalyst regions, even after CO2 co-reactants were depleted.

Complex Probabilistic Modeling with Recursive Relational Bayesian Networks

A number of representation systems have been proposed that extend the purely propositional Bayesian network paradigm with representation tools for some types of first-order probabilistic dependencies. Examples of such systems are dynamic Bayesian networks and systems for knowledge based model construction. We can identify the representation of probabilistic relational models as a common well-defined semantic core of such systems. Recursive relational Bayesian networks (RRBNs) are a framework for the representation of probabilistic relational models. A main design goal for RRBNs is to achieve greatest possible expressiveness with as few elementary syntactic constructs as possible. The advantage of such an approach is that a system based on a small number of elementary constructs will be much more amenable to a thorough mathematical investigation of its semantic and algorithmic properties than a system based on a larger number of high-level constructs. In this paper we show that with RRBNs we have achieved our goal, by showing, first, how to solve within that framework a number of non-trivial representation problems. In the second part of the paper we show how to construct from a RRBN and a specific query, a standard Bayesian network in which the answer to the query can be computed with standard inference algorithms. Here the simplicity of the underlying representation framework greatly facilitates the development of simple algorithms and correctness proofs. As a result we obtain a construction algorithm that even for RRBNs that represent models for complex first-order and statistical dependencies generates standard Bayesian networks of size polynomial in the size of the domain given in a specific application instance.

Transfer and Complexity in Artificial Grammar Learning

Implicit and explicit learning are sensitive to various degrees of complexity and abstractness, ranging from knowledge of first-order dependencies and specific surface structure to second-order dependencies and transfer. Three experiments addressed whether implicit learning is sensitive to this entire range of information or whether explicit knowledge becomes an important factor in cases of more complex learning. Experiment 1 used recognition and prediction to assess deliberate access to knowledge of letter patterns in an artificial grammar learning paradigm. Experiment 2 manipulated stimulus presentation and response in a sequence-based grammar learning paradigm. Learning can occur without awareness in cases of lesser complexity (such as learning first-order dependencies). However, more complex learning, such as that involved in learning second-order dependencies or in transfer to stimuli with the same underlying syntax but new surface features is linked to explicit knowledge. In contrast to Experiments 1 and 2 which assessed deliberate access to knowledge of the acquisition stimuli, Experiment 3 assessed deliberate access to knowledge of the transfer stimuli. Knowledge of initial trigrams in the transfer stimuli appears to play an important role in transfer. These findings are evaluated in terms of postulated implicit learning mechanisms.

Approximating optimally discrete probability distribution with kth-order dependency for combining multiple decisions

A probabilistic combination of K classifiers' decisions obtained from samples needs a ( K + 1) st-order probability distribution. Chow and Liu (1968) as well as Lewis (1959) proposed an approximation scheme of such a high-order distribution with a product of only first-order tree dependencies. However, if a classifier follows more than two classifiers, such first-order dependency does not estimate adequately a high-order distribution. Therefore, a new method is proposed to approximate optimally the ( K + 1) st-order distribution with a product set of kth-order dependencies where 1 ⩽ k ⩽ K, which are identified by a systematic dependency-directed approach. And also, a new method is presented to combine probabilistically multiple decisions with the product set of the kth-order dependencies, using a Bayesian formalism.

Developmental changes in the sequential behavior of interacting timber wolf pups

We used information statistics to quantify first-order sequential dependencies in the social behavior of two sibling wolf pups ( Canis lupus). Sequential dependencies in the behavior of the individual pups increased between the first sample (18–32 days of age) and two later samples taken from 34–53 days and 64–106 days of age. Sequential dependencies between pups were greatest during the second age sampled. We relate these findings to changes in the importance and style of interactions. In the first 32 days, social co-actions primarily involved mutual mouthing and pushing. Between 34 and 53 days, when interactions frequently became aggressive, pups were most responsive to the actions of their partner and tail-raising predicted biting. By 64 days, play-chasing was the mode. By this age pups had formed a relationship, were less attentive to the specific actions of their partner, and biting was no longer predicted by tail-raising. These results illustrate the importance of considering demand characteristics in distinguishing capabilities from performance, and suggest that changing social organization may underlie developmental changes in behavior. We also speculate that during these play-like interactions, pups may gain control over expressive displays.

Redox reactivity of the binuclear iron active site of porcine purple acid phosphatase (uteroferrin)

Redox interconversions (25 °C) of active FeIIFeIII purple acid phosphatase (PAPr), and the inactive FeIIIFeIII form (PAPo), have been explored at pH 5.0 (close to maximum activity), I= 0.100 M (NaCl). At this pH the PAPo–PAPr, couple has a reduction potential E°′ of 367 mV vs. normal hydrogen electrode. Whereas with [Co(phen)3]3+ as oxidant for PAPr first-order dependencies on both reactants are observed (k= 1.26 M–1 s–1), with [Fe(CN)6]3– saturation kinetics are obtained with association. K= 540 M–1, occurring prior to electron transfer, ket= 1.0 s–1. The latter reaction undergoes competitive inhibition with redox inactive [Cr(CN)6]3–(Kcr= 550 M–1) and [Mo(CN)8]4–(KMo= 1580 M–1) consistent with a positively charged locality on the protein surface influencing reactivity. With [Ru(NH3)6]2+ reduction of PAPo is too fast to monitor, but with the less strongly reducing [Ru(NH3)5(H2O)]2+ a rate law first order in both reactants (k= 2.2 × 105 M–1 s–1) is observed. Reactions of both these reductants with the less strongly oxidising phosphate bound PAPo–PO4 form (183 mV) were also studied. On reduction of PAPr with S2O42– a bleaching of the colour is observed consistent with FeIIFeII formation. After 30 min only 40% of the protein could be restored to one or other of the higher oxidation states, indicating loss of FeII.

Kinetic analysis of surface reduction in transition metal oxide single crystals

Abstract A mathematical treatment of the kinetics of metal oxide reduction is presented that is particularly applicable to single crystal substrates under conditions which promote slow, orderly removal of lattice oxygen at the crystal surface. The crystal is modeled as a semi-infinite plane with oxygen removal at the surface only and with Fickian diffusion from subsurface oxide into the depleted surface region. Removal of oxygen at the surface has been related to oxide surface concentration, as well as to oxygen vacancy concentration in an attempt to model the commonly observed induction period to reduction onset. Solutions to the models are given in closed analytic form for zeroth and first-order oxygen surface concentration dependencies and as an infinite, but converging, series in reaction time for the first and higher reaction orders. A numerical method is also used to extend the fit to longer times for which the infinite series method converges too slowly to be practically useful. As an example of the relevance of the kinetic analysis, the model is applied to data obtained on a NiO(100) substrate reduced under 1.0×10−7 to 1.3×10−6 Torr H2.

Kinetic analysis of surface reduction in transition metal oxide single crystals

A mathematical treatment of the kinetics of metal oxide reduction is presented that is particularly applicable to single crystal substrates under conditions which promote slow, orderly removal of lattice oxygen at the crystal surface. The crystal is modeled as a semi-infinite plane with oxygen removal at the surface only and with Fickian diffusion from subsurface oxide into the depleted surface region. Removal of oxygen at the surface has been related to oxide surface concentration, as well as to oxygen vacancy concentration in an attempt to model the commonly observed induction period to reduction onset. Solutions to the models are given in closed analytic form for zeroth and first-order oxygen surface concentration dependencies and as an infinite, but converging, series in reaction time for the first and higher reaction orders. A numerical method is also used to extend the fit to longer times for which the infinite series method converges too slowly to be practically useful. As an example of the relevance of the kinetic analysis, the model is applied to data obtained on a NiO(100) substrate reduced under 1.0×10 −7 to 1.3×10 −6 Torr H 2.

Use of markov-encoded sequential information in numerical signal detection

Twelve Ss made binary decisions with feedback on numbers from one of two normal distributions with equal variances and unequal means. Sequences of distribution choices corresponded to first-order two-state Markov processes with probabilities of change of state of p1 = p2 = .50, p1 = p2 = .75, and p1 = p2 = .25. Performance was best (in d’ terms) when p1 = p2 ≠ .50. First-order sequential response dependencies tended to mirror the first-order stimulus dependencies. Violations of a fixed cutoff point decision rule were concentrated in the region of the average critical point, with a bandwidth of about 1/2σ, in which violations were strikingly more frequent than would be expected if they had occurred randomly. These results imply that in this task Ss are using a criterion-band decision rule instead of a fixed cutoff point rule, and that they are basing decisions in the region of the criterion band on information extracted from the sequence of decisions presented to them. The average bandwidth is generally different from the optimum bandwidth used by an ideal O in combining the two sources of information.

SEQUENTIAL PATTERNS IN POST‐REINFORCEMENT PAUSES ON FIXED‐INTERVAL SCHEDULES OF FOOD1

Responding by one pigeon was reinforced with food on fixed-interval schedules of 30, 60, and 300 sec duration. A second pigeon was studied under fixed-interval durations of 60 and 300 sec. For both birds, the average post-reinforcement pause was one-half the duration of the fixed interval. Autocorrelation coefficients revealed first-order sequential dependencies in series of post-reinforcement pauses. On the 300-sec fixed-interval schedule, successive post-reinforcement tended to alternate between long and short durations. At the shorter fixed-interval durations there was less evidence of alternation sequences. A second experiment was conducted to determine if the time intervals between the first response after reinforcement and the next reinforcement (the work periods) were responsible for the alternation patterns in the series of post-reinforcement pauses. To evaluate the role of the work period, several procedures were used to modify the work period from that obtained on the fixed-interval 300-sec schedule. Adding a schedule to the fixed-interval schedule that set the minimum amount of time that could elapse between the first response after reinforcement and the next reinforcement eliminated the alternation pattern. Control schedules indicated that the elimination of alternation patterns resulted from constraints on the work period per se and not from confounded changes in the interreinforcement intervals.

Acquisition and performance as a function of uncertainty and structure in serial tracking tasks

Criterion performance and response organization were evaluated in skilled tasks involving the pursuit tracking of step-function inputs. Both the structure and degree of first-order sequential dependencies among target events were manipulated in a factorial design. Tracking error was found to be a function of the degree of uncertainty and of the task structure (one vs two low-probability sequential alternatives) and the latter could not be attributed to the degree of uncertainty, per se. Furthermore, detailed analysis of continuous response records revealed differences in response strategies as a function of task structure. These differences accounted, in part, for difference in tracking performance and discrepancies between the present and prior research.

Two-choice probability learning: Test of a finite markov chain and linear operator model

Human subjects were trained in the noncontingent binary choice situation under one of two levels of event probability (π = .6, .8) and payoff (0c, 1c). These data were used to evaluate the ability of the Reinforcement-Extinction (R-E) Model and the Weak-Strong (W-S) Conditioning Model to describe marginal and first-order response-event dependencies throughout learning and second-order response-event dependencies and run curves during the last 100 trials. Both models described asymptotic statistics about equally well with largest discrepancies being in the fit-to-run curves. Generally, poorer preasymptotic fits were attributed to too rapid a rate of approach of the predicted statistics to their asymptotic values. Parameter estimates for both models varied with π and payoff throughout learning. From these estimates it was inferred that learning occurs on correct and incorrect trials; correct trials appeared more important in the R-E model while incorrect trials assumed major importance for the W-S model, especially prior to overshooting.

First-order response dependencies at a differential brightness threshold.

First-order response dependencies at a differential brightness threshold.

SEQUENTIAL RESPONSE EFFECTS IN THE WHITE RAT DURING CONDITIONING AND EXTINCTION ON A DRL SCHEDULE.

Sequential IRT data were obtained for three rats on a DRL 60-sec reinforcement schedule. It was found that first-order sequential dependencies exist under this schedule, including the partial dependence of the length of any given IRT on the length of the preceding IRT. The sequential analysis also served to extend the finding in the literature, based on frequency distributions, that the likelihood of a reinforced IRT is greater after a reinforced IRT than a non-reinforced IRT. Rapid extinction and reconditioning were obtained.

Variables affecting activity of molybdena-alumina hydroforming catalyst in aromatization of cyclohexane

An exploratory study has been made to determine the nature and magnitude of the more obvious variations in the activity of a single batch of catalyst. A pelleted, coprecipitated, gel-type catalyst similar to that used in many commercial catalytic reforming units was studied in a differential reactor. Apparently the molybdenum oxide promoter was at a higher level of activity when the oxide was in a reduced state, the improvement being directly proportional to the degree of molar reduction of the oxide. Deposition of coke on the catalyst, which reduced activity exponentially, appeared to be a surface-controlled reaction and to involve a dual site mechanism. Increased hydrogen to hydrocarbon molar feed ratios reduced carbon deposition in direct proportion to the associated increase in hydrogen partial pressure but also reduced conversion because of the lowered reactant partial pressure. Catalyst regeneration appeared to occur in two distinct steps. In the first step, the rate had a second order dependency on coke concentration. In the second step, for which first-order dependencies on coke concentration and oxygen partial pressure were observed, mass transfer seemed to be the controlling mechanism. No improvement in catalyst activity was noted in the removal of carbon in this second step.