Measure Of Fluctuations Research Articles

Impact of Disease Cyclicity on Quality-of-Life Impairment – A Mixed Method Explanatory Study

BackgroundDisease cyclicity, a composite measure of unpredictable and daily fluctuations of symptoms, strongly predicts quality-of-life (QoL) impairment. ObjectiveTo explore the mechanisms by which cyclicity impacts QoL. Methods1:1 semi-structured interviews were conducted and qualitatively analyzed using grounded theory. Common themes were identified and used to derive a theoretical framework. Results23 adults, (median age 28.5, range 20-72) with a range of chronic inflammatory diseases including eczema, psoriasis and inflammatory arthritis were prospectively recruited. Cyclicity, characterized by unpredictability, uncontrollability, and lability, contributed to a varying productivity and inconsistent ability in performing daily activities. Challenges intensified if external expectations of the patient were inconsistent with their fluctuating ability. Coping abilities, shaped by internal and external factors, moderated the relationship between cyclicity and QoL impairment. LimitationsQualitative interviews assume patients have accurate insights into their own behaviours. The generalizability of findings may be limited in other populations. ConclusionDisease cyclicity defines many inflammatory diseases. This study provides a theoretical framework for understanding and managing the challenges patients with a cyclical condition face.

Pharmacological manipulation of neurotransmitter activity induces disparate effects on cerebral blood flow and resting-state fluctuations

Abstract Functional MRI methods can assess aspects of drug-induced brain response. Resting blood oxygenation level dependent (BOLD) fMRI and arterial spin labeling (ASL) perfusion MRI indirectly measure brain function through the coupling of activity to cerebral blood flow (CBF) and oxygenation but their relative sensitivity has not been directly compared. We assessed changes in resting measures of BOLD and ASL MRI in response to two neurotransmitter modulators: citalopram, a selective serotonin reuptake inhibitor, and alprazolam, a positive allosteric modulator of GABA type A receptor. Thirty healthy subjects were imaged in a placebo-controlled study, with N=20 subjects receiving each treatment as part of an incomplete block design. Time-averaged CBF images from ASL and measures of resting-state fluctuations of BOLD and ASL images were assessed for significant effects. Following acute citalopram administration, analysis of the ASL data showed a reduction in time-averaged regional CBF in regions associated with high levels of 5-HT1A receptor density. In contrast, following alprazolam administration, BOLD amplitude of low frequency fluctuations showed a highly significant and cortically widespread increase, consistent with the distribution of GABA-A receptors. Only a marginal decrease in ASL CBF was detected after alprazolam intake. BOLD and ASL are each sensitive to drugs targeting neurotransmitter systems, but appear to reflect different aspects of neural metabolism and the balance between excitatory and inhibitory activity. Accordingly, their combination may best capture the effects of neurotransmitter modulations, and thus be advantageous for pharmacological MRI studies.

Consensus decision making on a complete graph: complex behaviour from simple assumptions

In this paper we investigate a model of consensus decision making [Hartnett A. T., et al., Phys. Rev. Lett., 2016, 116, 038701] following a statistical physics approach presented in [Sarkanych P., et al., Phys. Biol., 2023, 20, 045005]. Within this approach, the temperature serves as a measure of fluctuations, not considered before in the original model. Here, we discuss the model on a complete graph. The main goal of this paper is to show that an analytical description may lead to a very rich phase behaviour, which is usually not expected for a complete graph. However, the variety of individual agent (spin) features - their inhomogeneity and bias strength - taken into account by the model leads to rather non-trivial collective effects. We show that the latter may emerge in a form of continuous or abrupt phase transitions sometimes accompanied by re-entrant and order-parameter flipping behaviour. In turn, this may lead to appealing interpretations in terms of social decision making. We support analytical predictions by numerical simulation. Moreover, while analytical calculations are performed within an equilibrium statistical physics formalism, the numerical simulations add yet another dynamical feature - local non-linearity or conformity of the individual to the opinion of its surroundings. This feature appears to have a strong impact both on the way in which an equilibrium state is approached as well as on its characteristics.

Linking Electronic and Structural Disorder Parameters to Carrier Transport in a Modern Conjugated Polymer.

Understanding charge transport in conjugated polymers is crucial for the development of next-generation organic electronic applications. It is presumed that structural disorder in conjugated polymers originating from their semicrystallinity, processing, or polymorphism leads to a complex energetic landscape that influences charge carrier transport properties. However, the link between polymer order parameters and energetic landscape is not well established experimentally. In this work, we successfully link statistical surveys of the local polymer electronic structure with paracrystalline structural disorder, a measure of statistical fluctuations away from the ideal polymer packing structure. We use scanning tunneling microscopy/spectroscopy to measure spatial variability in electronic band edges in PM6 films, a high-performance conjugated polymer, and find that films with higher paracrystallinity exhibit greater electronic disorder, as expected. In addition, we show that macroscopic charge carrier mobility in field effect transistors and and trap influence in hole-only diode devices is positively correlated with these microscopic structural and electronic parameters.

Optimized reversible data hiding technique based on multidirectional prediction error histogram and fluctuation-based adaptation

Reversible Data Hiding Techniques (RDH) play an increasingly pivotal role in the field of cybersecurity. Overlooking the properties of the carrier image and neglecting the influence of texture can lead to undesirable distortions and irreversible data hiding. In this paper, a novel block-based RDH technique is proposed that harnesses the relative correlation between multidirectional prediction error histograms (MPEH) and pixel fluctuation values to mitigate undesirable distortions and enable RDH, thereby ensuring heightened security and efficiency in the distribution process and improving the robustness of the block-based RDH technique. The proposed technique uses a combination of pixel fluctuation and local complexity measures to determine the best embedding locations within smooth regions based on the cumulative peak regions of the MPEH with the lowest fluctuation values. Similarly, during the extraction process, the same optimal embedding locations are identified within smooth regions. The multidirectional prediction error histograms are then used to accurately extract the hidden data from the pixels with lower fluctuation values. Overall, the experimental results highlight the effectiveness and superiority of the proposed technique in various aspects of data embedding and extraction, and demonstrate that the proposed technique outperforms other state-of-the-art RDH techniques in terms of embedding capacity, image quality, and robustness against attacks. The average Peak Signal-to-Noise Ratio (PSNR) achieved with an embedding capacity ranging from 0.5×104 bits to 5×104 bits is 52.72 dB. Additionally, there are no errors in retrieving the carrier image and secret data.

Symmetric circulant matrix decomposition-based multivariable group sparse coding for rolling bearing fault diagnosis

Singular value decomposition technique proves its effectiveness in mechanical signal analysis by decomposing the test signal into a series of singular spectral components of different frequency bands. Nevertheless, how to adapt this technology to the needs of cyclo-nonstationary signal and how to set the decomposition number while maintaining detailed features to obtain the optimal component containing the most fault information, remains an important issue that needs to be addressed in the field of mechanical fault diagnosis. To overcome these disadvantages, the symmetric circulant matrix decomposition (SCMD) is presented. Two main ideas structure the present technique. Firstly, symmetric circulant matrix is used to generate eigenvectors, which will better adapt to the cyclo-nonstationary signal associated with the structural symmetry of rotating machinery. Then, an impulse fluctuation measure is established to adaptively search for the decomposition number and extract the optimal component. Moreover, to better improve the impulse extraction effect of SCMD, the multivariate group sparse coding based on the multivariate correlation characteristics and intra group sparsity characteristics of impulse signals is proposed, which can enhance impulse features while preserving fault details as much as possible. The reliability and feasibility of the proposed method are verified by the experimental signals. The comparison with several classic methods shows that this method is more effectiveness in weak feature extraction.

The Relationship of Hedonic Hunger, Macronutrient Balance, Nutrition Knowledge, and Body Image and Weight Control with Dietary Intake in Student Athletes and Exercisers.

Dietary intake is known to impact athletic performance. The factors that influence dietary intake have been investigated widely, but their collective effect has not been examined. The primary aim of this cross-sectional study was to assess the relationship between dietary intake and nutrition knowledge, body image, weight control, macronutrient balance, and hedonic hunger. Forty-two student athletes or active individuals were recruited through contact with sporting organisations and course coordinators, advertising via twitter, and flyers posted within university buildings. Nutrition knowledge, body image, weight control, macronutrient balance, and hedonic hunger were measured using the Abridged Nutrition for Sport Knowledge Questionnaire, Body Image Disturbance Questionnaire and Contour Drawing Rating Scale, a Weight Fluctuation Measure, Australian Eating Score, and Power of Food Scale, respectively. Hierarchical regression analysis, correlation testing, and mean difference testing were applied. Carbohydrate intake, body image disturbance scores, weight fluctuation, and hedonic hunger for food tasted had a significant relationship (R2 = 64.6%, Adj R2 = 0.608%, p < 0.001) with dietary energy intake. Student athletes' dietary intakes are influenced by multiple potentially modifiable factors. Future studies should use larger sample sizes, with interventions focusing on individual modifiable factors to determine how dietary intake can be most significantly impacted.

Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals

Heart diseases are leading to death across the globe. Exact detection and treatment for heart disease in its early stages could potentially save lives. Electrocardiogram (ECG) is one of the tests that take measures of heartbeat fluctuations. The deviation in the signals from the normal sinus rhythm and different variations can help detect various heart conditions. This paper presents a novel approach to cardiac disease detection using an automated Convolutional Neural Network (CNN) system. Leveraging the Scale-Invariant Feature Transform (SIFT) for unique ECG signal image feature extraction, our model classifies signals into three categories: Arrhythmia (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). The proposed model has been evaluated using 96 Arrhythmia, 30 CHF, and 36 NSR ECG signals, resulting in a total of 162 images for classification. Our proposed model achieved 99.78% accuracy and an F1 score of 99.78%, which is among one of the highest in the models which were recorded to date with this dataset. Along with the SIFT, we also used HOG and SURF techniques individually and applied the CNN model which achieved 99.45% and 78% accuracy respectively which proved that the SIFT–CNN model is a well-trained and performed model. Notably, our approach introduces significant novelty by combining SIFT with a custom CNN model, enhancing classification accuracy and offering a fresh perspective on cardiac arrhythmia detection. This SIFT–CNN model performed exceptionally well and better than all existing models which are used to classify heart diseases.

Energy and system size dependence of strongly intensive fluctuation measures in heavy-ion collisions at FAIR energies

Event-by-event fluctuations of multiplicity and transverse momentum of charged hadrons produced in heavy-ion collisions at FAIR energies, 10A, 20A, 30A, and 40A GeV are studied in the framework of relativistic transport model, URQMD. Dependence of two families of strongly intensive measures of multiplicity(N) and transverse momentum (p T) fluctuations, Δ[p T, N] and Σ[p T, N], on collision centrality, centrality bin-widths and pseudorapidity windows are examined. Attempts are also made to study NN, N p T, and p T p T fluctuations using two window analysis method. The findings suggest that the measure, Δ[p T, N] be dealt with proper selection of centrality intervals. This measure also exhibits a strong dependence on the widths of η windows. The variable Σ[p T, N], however, is observed to be insensitive to the centrality bin-widths and shows a variation of <5% with the widths of η windows. The analysis of data after event mixing gives Δ[p T, N] and Σ[p T, N] values as ∼1 irrespective of the widths of η windows and collision centrality, as predicted by a model of independent particle emission, IPM. The study of joint fluctuations of the two quantities on two η windows separated in η space, reveals that Σ[N F, N B] values are ∼1 irrespective of the position of η windows whereas, the values of and firstly increase with η sep and later acquire saturations. The observed trend of centrality dependence of and agrees fairly well with those observed in MC simulated studies carried out for AA collisions at LHC energies in the framework model of string fusion.

24 Mindfulness-based cognitive therapy enhances executive control in recurrent depression in a randomized wait-list controlled trial

Objective:Adults with recurrent depression have been shown to have cognitive deficits also while in remission. Thus, it has been suggested that with a chronic course of depression, poorer executive control can be a vulnerability factor for depressive relapse. This has led to research on how cognitive remediation training can protect against recurrent depressive episodes. Findings indicate that such training has short term effects on cognitive functioning, and small effect on depression symptoms. Less focus has been on how “standard” psychotherapy can have positive effects on executive and attentional control. Mindfulness-based cognitive therapy (MBCT) has been shown to be as effective as antidepressant medication in preventing relapses of depressive episodes. Mindfulness training in healthy samples seems to improve executive and attentional control. However, the few studies of MBCT in recurrent depression show mixed effects on executive and attentional control. As far as we know, no prior study has investigated the effect of MBCT in recurrent depression with the revised version of the attention network test (ANT-R). In a randomized controlled trial, we expected that the MBCT group would show enhanced executive control and lower levels of attentional fluctuations than the wait-list controls (WLC) from pre (T0) to post (T1) treatment. We further investigated if positive effects of MBCT on executive and attentional control were associated with reduction in depression symptoms.Participants and Methods:Adults with recurrent depression in partial or full remission (N = 64) were randomized to MBCT or WLC. In the MBCT and WLC groups, 25 and 29, respectively, performed the ANT-R at T0 and T1. The attention network reaction time scores of executive control, alerting, and orienting were calculated in addition to attention fluctuations scores of intra-individual reaction time variability (IIVRT) and exgaussian-mean of longer reaction times (TAU). Self-reported depression symptoms were measured with BDI-II. The two groups were compared at baseline on full-scale IQ (WASI), executive control (D-KEFS Stroop), and processing speed (D-KEFS TMT).Results:The MBCT and WCL groups did not differ significantly in age or gender distribution, education, full-scale IQ or in baseline executive and attentional control as measured with the ANT-R, Stroop and TMT. The MBCT group showed a higher efficiency in conflict detection as measured with the executive control score from T0 to T1 compared to the WLC. This positive effect of MBCT on executive control was independent from the greater reductions in depression symptoms in the MBCT group compared to in the WLCs. However, reduction in depression symptoms at T1 was associated with enhanced efficiency in responding to alerting cues in conflict detection. No effects of MBCT compared to WLC were found at T1 on the attention fluctuation measures (IIVRT and TAU).Conclusions:The findings from the current study indicates that MBCT enhances executive control in adults with recurrent depression. As such, MBCT may target an important cognitive vulnerability factor in the chronic course of recurrent depression that may contribute to its efficacy in preventing depressive relapses. It was also observed that reductions in depression symptoms led to higher alertness in conflict detection.

Molecular noise-induced activator-inhibitor duality in enzyme inhibition kinetics.

Classical theories of enzyme inhibition kinetics predict a monotonic decrease in the mean catalytic activity with the increase in inhibitor concentration. The steady-state result, derived from deterministic mass action kinetics, ignores molecular noise in enzyme-inhibition mechanisms. Here, we present a stochastic generalization of enzyme inhibition kinetics to mesoscopic enzyme concentrations by systematically accounting for molecular noise in competitive and uncompetitive mechanisms of enzyme inhibition. Our work reveals an activator-inhibitor duality as a non-classical effect in the transient regime in which inhibitors tend to enhance enzymatic activity. We introduce statistical measures that quantify this counterintuitive response through the stochastic analog of the Lineweaver-Burk plot that shows a merging of the inhibitor-dependent velocity with the Michaelis-Menten velocity. The statistical measures of mean and temporal fluctuations - fractional enzyme activity and waiting time correlations - show a non-monotonic rise with the increase in inhibitors before subsiding to their baseline value. The inhibitor and substrate dependence of the fractional enzyme activity yields kinetic phase diagrams for non-classical activator-inhibitor duality. Our work links this duality to a molecular memory effect in the transient regime, arising from positive correlations between consecutive product turnover times. The vanishing of memory in the steady state recovers all the classical results.

Early universe: Gravitational waves and tensor metric fluctuations

In this work, we develop a model for the early universe in which preinflation is followed by a warming epoch and a subsequent sub-exponential quintessential expansion. We demonstrate that gravitational waves (GW) generated at the birth of the universe leave an imprint of that event, which is significantly suppressed a few Planckian times after that moment. We calculate the strengths of both, the total transverse GW and the b-polarization, for each mode. We use a nonlinear quantum algebra to calculate the expectation value of the quantum metric tensor perturbations: δgˆαβ, which are induced by the geometric quantum field with which we extend the Riemann manifold. A measure for the temperature fluctuations at the Hubble horizon is proposed: δT/Th=−bgαβδgˆαβ(k∗,t).

Local structural and functional MRI markers of compulsive behaviors and obsessive-compulsive disorder diagnosis within striatum-based circuits.

Obsessive-compulsive disorder (OCD) is a classic disorder on the compulsivity spectrum, with diverse comorbidities. In the current study, we sought to understand OCD from a dimensional perspective by identifying multimodal neuroimaging patterns correlated with multiple phenotypic characteristics within the striatum-based circuits known to be affected by OCD. Neuroimaging measurements of local functional and structural features and clinical information were collected from 110 subjects, including 51 patients with OCD and 59 healthy control subjects. Linked independent component analysis (LICA) and correlation analysis were applied to identify associations between local neuroimaging patterns across modalities (including gray matter volume, white matter integrity, and spontaneous functional activity) and clinical factors. LICA identified eight multimodal neuroimaging patterns related to phenotypic variations, including three related to symptoms and diagnosis. One imaging pattern (IC9) that included both the amplitude of low-frequency fluctuation measure of spontaneous functional activity and white matter integrity measures correlated negatively with OCD diagnosis and diagnostic scales. Two imaging patterns (IC10 and IC27) correlated with compulsion symptoms: IC10 included primarily anatomical measures and IC27 included primarily functional measures. In addition, we identified imaging patterns associated with age, gender, and emotional expression across subjects. We established that data fusion techniques can identify local multimodal neuroimaging patterns associated with OCD phenotypes. The results inform our understanding of the neurobiological underpinnings of compulsive behaviors and OCD diagnosis.

Local measures of fluctuations in inhomogeneous liquids: statistical mechanics and illustrative applications

We show in detail how three one-body fluctuation profiles, namely the local compressibility, the local thermal susceptibility, and the reduced density, can be obtained from a statistical mechanical many-body description of classical particle-based systems. We present several different and equivalent routes to the definition of each fluctuation profile, facilitating their explicit numerical calculation in inhomogeneous equilibrium systems. This underlying framework is used for the derivation of further properties such as hard wall contact theorems and novel types of inhomogeneous one-body Ornstein–Zernike equations. The practical accessibility of all three fluctuation profiles is exemplified by grand canonical Monte Carlo simulations that we present for hard sphere, Gaussian core and Lennard–Jones fluids in confinement.

Individual bias and fluctuations in collective decision making: from algorithms to Hamiltonians

In this paper, we reconsider the spin model suggested recently to understand some features of collective decision making among higher organisms (Hartnett et al 2016 Phys. Rev. Lett. 116 038701). Within the model, the state of an agent i is described by the pair of variables corresponding to its opinion and a bias ω i toward any of the opposing values of S i . Collective decision making is interpreted as an approach to the equilibrium state within the nonlinear voter model subject to a social pressure and a probabilistic algorithm. Here, we push such a physical analogy further and give the statistical physics interpretation of the model, describing it in terms of the Hamiltonian of interaction and looking for the equilibrium state via explicit calculation of its partition function. We show that, depending on the assumptions about the nature of social interactions, two different Hamiltonians can be formulated, which can be solved using different methods. In such an interpretation the temperature serves as a measure of fluctuations, not considered before in the original model. We find exact solutions for the thermodynamics of the model on the complete graph. The general analytical predictions are confirmed using individual-based simulations. The simulations also allow us to study the impact of system size and initial conditions on the collective decision making in finite-sized systems, in particular, with respect to convergence to metastable states.

Bivariate moments of the two-point correlation function for embedded Gaussian unitary ensemble with k-body interactions.

Embedded random matrix ensembles with k-body interactions are well established to be appropriate for many quantum systems. For these ensembles the two point correlation function is not yet derived, though these ensembles are introduced 50 years back. Two-point correlation function in eigenvalues of a random matrix ensemble is the ensemble average of the product of the density of eigenvalues at two eigenvalues, say E and E^{'}. Fluctuation measures such as the number variance and Dyson-Mehta Δ_{3} statistic are defined by the two-point function and so also the variance of the level motion in the ensemble. Recently, it is recognized that for the embedded ensembles with k-body interactions the one-point function (ensemble averaged density of eigenvalues) follows the so called q-normal distribution. With this, the eigenvalue density can be expanded by starting with the q-normal form and using the associated q-Hermite polynomials He_{ζ}(x|q). Covariances S_{ζ}S_{ζ^{'}}[over ¯] (overline representing ensemble average) of the expansion coefficients S_{ζ} with ζ≥1 here determine the two-point function as they are a linear combination of the bivariate moments Σ_{PQ} of the two-point function. Besides describing all these, in this paper formulas are derived for the bivariate moments Σ_{PQ} with P+Q≤8, of the two-point correlation function, for the embedded Gaussian unitary ensembles with k-body interactions [EGUE(k)] as appropriate for systems with m fermions in N single particle states. Used for obtaining the formulas is the SU(N) Wigner-Racah algebra. These formulas with finite N corrections are used to derive formulas for the covariances S_{ζ}S_{ζ^{'}}[over ¯] in the asymptotic limit. These show that the present work extends to all k values, the results known in the past in the two extreme limits with k/m→0 (same as q→1) and k=m (same as q=0).

Study of multiplicity fluctuations in terms of strongly intensive quantity in forward-backward zone

A completely new study of forward-backward multiplicity fluctuation of produced pions in nuclear emulsion detector has been carried out by the method of strongly intensive quantity ΣFB for 16O-AgBr, 28Si-AgBr and 32S-AgBr interactions at 4.5 AGeV/c. Experimental analysis has been compared using MC-RAND events and UrQMD simulated events. UrQMD model could not reproduce the experimental results. Outcome of this analysis supports the presence of dynamical fluctuations in multiparticle production at 4.5 AGeV/c. The results on strongly intensive fluctuation measures at a lower energy show much larger fluctuations in comparison to the higher energy data studied so far.

Ecological validity of cognitive fluctuations in dementia with Lewy bodies.

Cognitive fluctuations are a core clinical feature of dementia with Lewy bodies (DLB), but their contribution to the everyday functioning difficulties evident DLB are not well understood. The current study evaluated whether intraindividual variability across a battery of neurocognitive tests (intraindividual variability-dispersion) and daily cognitive fluctuations as measured by informant report are associated with worse daily functioning in DLB. The study sample included 97 participants with consensus-defined DLB from the National Alzheimer's Coordinating Center (NACC). Intraindividual variability-dispersion was measured using the coefficient of variation, which divides the standard deviation of an individual's performance scores across 12 normed neurocognitive indices from the NACC neuropsychological battery by that individual's performance mean. Informants reported on daily cognitive fluctuations using the Mayo Fluctuations Scale (MFS) and on daily functioning using the functional activities questionnaire (FAQ). Logistic regression identified a large univariate association of intraindividual variability-dispersion and presence of daily cognitive fluctuations on the MFS (Odds Ratio = 73.27, 95% Confidence Interval = 1.38, 3,895.05). Multiple linear regression demonstrated that higher intraindividual variability-dispersion and presence of daily cognitive fluctuations as assessed by the MFS were significantly and independently related to worse daily functioning (FAQ scores). Among those with DLB, informant-rated daily cognitive fluctuations and cognitive fluctuations measured in the clinic (as indexed by intraindividual variability-dispersion across a battery of tests) were independently associated with poorer everyday functioning. These data demonstrate ecological validity in measures of cognitive fluctuations in DLB.

Marsh birds as ecological performance indicators for Lake Ontario outflow regulation

Water-level regulation can have significant impacts on coastal wetland ecosystems. In this study we sought to update marsh-bird-based ecological performance indicators (PIs) that support adaptive management of long-term outflow regulation for Lake Ontario. Previous PIs established in the mid-2000 s were based on single species and monitoring them required data not currently being collected at broad scales. We therefore focused on developing and validating community-level PIs using data from an ongoing, long-term, basin-wide monitoring program, the Coastal Wetland Monitoring Program (CWMP). After identifying species with documented responses to variation in water levels in the literature, we considered a suite of potential PIs by first examining correlations with both annual mean water levels and measures of interannual water-level fluctuations. We then used a mixed-modelling framework to determine which highly correlated PIs exhibited statistically significant relationships with water-level variables. Having established significant effects of water levels on the candidate PIs, we performed a power-sensitivity analysis to determine the degree of change in each PI that can be detected based on current CWMP sampling. From these analyses, we propose six potential marsh-bird based PIs: sum total abundance of sensitive marsh-obligate species, richness of sensitive marsh-obligate species, and abundance of each of red-winged blackbird (Agelaius phoeniceus), marsh wren (Cistothorus palustris), common gallinule (Gallinula galeata), and least bittern (Ixobrychus exilis). Of these, the community-based PIs of sum total abundance and richness of sensitive species appear most suitable for assessing the marsh-bird community response to outflow regulation on Lake Ontario.

Understanding the physics of hydrophobic solvation.

Simulations of water near extended hydrophobic spherical solutes have revealed the presence of a region of depleted density and accompanying enhanced density fluctuations. The physical origin of both phenomena has remained somewhat obscure. We investigate these effects employing a mesoscopic binding potential analysis, classical density functional theory (DFT) calculations for a simple Lennard-Jones solvent, and Grand Canonical Monte Carlo (GCMC) simulations of a monatomic water (mw) model. We argue that the density depletion and enhanced fluctuations are near-critical phenomena. Specifically, we show that they can be viewed as remnants of the critical drying surface phase transition that occurs at bulk liquid-vapor coexistence in the macroscopic planar limit, i.e., as the solute radius Rs → ∞. Focusing on the radial density profile ρ(r) and a sensitive spatial measure of fluctuations, the local compressibility profile χ(r), our binding potential analysis provides explicit predictions for the manner in which the key features of ρ(r) and χ(r) scale with Rs, the strength of solute-water attraction ɛsf, and the deviation from liquid-vapor coexistence of the chemical potential, δμ. These scaling predictions are confirmed by our DFT calculations and GCMC simulations. As such, our theory provides a firm basis for understanding the physics of hydrophobic solvation.