Persistence of atoms in molecules: there is room beyond electron densities

Evidence that the electronic structure of atoms persists in molecules to a much greater extent than has been usually admitted is presented. This is achieved by resorting to N-electron real-space descriptors instead of one- or at most two-particle projections like the electron or exchange-correlation densities. Here, the 3N-dimensional maxima of the square of the wavefunction, the so-called Born maxima, are used. Since this technique is relatively unknown to the crystallographic community, a case-based approach is taken, revisiting first the Born maxima of atoms in their ground state and then some of their excited states. It is shown how they survive in molecules and that, beyond any doubt, the distribution of electrons around an atom in a molecule can be recognized as that of its isolated, in many cases excited, counterpart, relating this fact with the concept of energetic promotion. Several other cases that exemplify the applicability of the technique to solve chemical bonding conflicts and to introduce predictability in real-space analyses are also examined.

A Level Set-Based Model for Image Segmentation under Geometric Constraints and Data Approximation

In this paper, we propose a new model for image segmentation under geometric constraints. We define the geometric constraints and we give a minimization problem leading to a variational equation. This new model based on a minimal surface makes it possible to consider many different applications from image segmentation to data approximation.

A Predictive Model of Capillary Forces and Contact Diameters between Two Plates Based on Artificial Neural Network

Many efforts have been devoted to the forecasting of the capillary force generated by capillary adsorption between solids, which is fundamental and essential in the fields of micro-object manipulation and particle wetting. In this paper, an artificial neural network (ANN) model optimized by a genetic algorithm (GA-ANN) was proposed to predict the capillary force and contact diameter of the liquid bridge between two plates. The mean square error (MSE) and correlation coefficient (R²) were employed to evaluate the prediction accuracy of the GA-ANN model, theoretical solution method of the Young-Laplace equation and simulation approach based on the minimum energy method. The results showed that the values of MSE of capillary force and contact diameter using GA-ANN were 10.3 and 0.0001, respectively. The values of R² were 0.9989 and 0.9977 for capillary force and contact diameter in regression analysis, respectively, demonstrating the accuracy of the proposed predictive model. The sensitivity analysis was conducted to investigate the influence of input parameters, including liquid volume and separation distance, on the capillary force and contact diameter. The liquid volume and separation distance played dominant roles in affecting the capillary force and contact diameter.

An Order Reduction Design Framework for Higher-Order Binary Markov Random Fields

The order reduction method is an important approach to optimize higher-order binary Markov random fields (HoMRFs), which are widely used in information theory, machine learning and image analysis. It transforms an HoMRF into an equivalent and easier reduced first-order binary Markov random field (RMRF) by elaborately setting the coefficients and auxiliary variables of RMRF. However, designing order reduction methods is difficult, and no previous study has investigated this design issue. In this paper, we propose an order reduction design framework to study this problem for the first time. Through study, we find that the design difficulty mainly lies in that the coefficients and variables of RMRF must be set simultaneously. Therefore, the proposed framework decomposes the design difficulty into two processes, and each process mainly considers the coefficients or auxiliary variables of RMRF. Some valuable properties are also proven. Based on our framework, a new family of 14 order reduction methods is provided. Experiments, such as synthetic data and image denoising, demonstrate the superiority of our method.

RSAD2: An exclusive target protein for Zika virus comparative modeling, characterization, energy minimization and stabilization

Objective

The major purpose of the present study was to predict the structure of Radical s-adenosyl-L-methionine Domain 2 (RSAD2), the most targeted protein of the Zika virus using comparative modeling, to validate the models that were generated and molecular dynamics (MD) simulations were performed.

Methods

The secondary structure of RSAD2 was estimated using the Garnier-Osguthorpe-Robson, Self-Optimized Prediction method with Alignment, and Position-Specific Iterative-Blast based secondary structure prediction algorithms. The best of them were preferred based on their DOPE score, then three-dimensional structure identification using SWISS-MODEL and the Protein Homology/Analogy Recognition Engine (Phyre2) server. SAVES 6.0 was used to validate the models, and the preferred model was then energetically stabilized. The model with least energy minimization was used for MD simulations using iMODS.

Results

The model predicted using SWISS-MODEL was determined as the best among the predicted models. In the Ramachandran plot, there were 238 residues (90.8%) in favored regions, 23 residues (8.8%) in allowed regions, and 1 residue (0.4%) in generously allowed regions. Energy minimization was calculated using Swiss PDB viewer, reporting the SWISS-MODEL with the lowest energy (E = -18439.475 KJ/mol) and it represented a stable structure conformation at three-dimensional level when analyzed by MD simulations.

Conclusion

A large amount of sequence and structural data is now available, for tertiary protein structure prediction, hence implying a computational approach in all the aspects becomes an opportunistic strategy. The best three-dimensional structure of RSAD2 was built and was confirmed with energy minimization, secondary structure validation and torsional angles stabilization. This modeled protein is predicted to play a role in the development of drugs against Zika virus infection.

Ground State Properties of the Wide Band Gap Semiconductor Beryllium Sulfide (BeS)

We report the results from self-consistent calculations of electronic, transport, and bulk properties of beryllium sulfide (BeS) in the zinc-blende phase, and employed an ab-initio local density approximation (LDA) potential and the linear combination of atomic orbitals (LCAO). We obtained the ground state properties of zb-BeS with the Bagayoko, Zhao, and Williams (BZW) computational method, as enhanced by Ekuma and Franklin (BZW-EF). Our findings include the electronic energy bands, the total (DOS) and partial (pDOS) densities of states, electron and hole effective masses, the equilibrium lattice constant, and the bulk modulus. The calculated band structure clearly shows that zb-BeS has an indirect energy band gap of 5.436 eV, from Γ to a point between Γ and X, for an experimental lattice constant of 4.863 Å. This is in excellent agreement with the experiment, unlike the findings of more than 15 previous density functional theory (DFT) calculations that did not perform the generalized minimization of the energy functional, required by the second DFT theorem, which is inherent to the implementation of our BZW-EF method.

Probing the Increased Virulence of Severe Acute Respiratory Syndrome Coronavirus 2 B.1.617 (Indian Variant) From Predicted Spike Protein Structure

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to an outbreak of a pandemic worldwide. The spike (S) protein of SARS-CoV-2, which plays a key role in the receptor recognition and cell membrane fusion process, is composed of two subunits, S1 and S2. The S1 subunit contains a receptor-binding domain that recognizes and binds to the host receptor angiotensin-converting enzyme 2 (ACE2), while the S2 subunit mediates viral cell membrane fusion with the cell membrane and subsequent entry into cells. Mutations in the spike protein (S) are of particular interest due to their potential for reduced susceptibility to neutralizing antibodies or increasing the viral transmissibility and infectivity. Recently, many mutations in the spike protein released new variants, including the Delta and Kappa ones (known as the Indian variants). The variants Delta and Kappa are now of most recent concern because of their well-increased infectivity, both a spin-off of the B.1.617 lineage, which was first identified in India in October 2020. This study employed homology modeling to probe the potential structural effects of the mutations. It was found that the mutations, Leu452Arg, Thr478Lys, and Glu484Gln in the spike protein increase the affinity for the hACE2 receptor, which explains the greater infectivity of the SARS-Cov-2 B.1.617 (Indian Variant).

Energy Efficient SWIPT Based Mobile Edge Computing Framework for WSN-Assisted IoT

With the increasing deployment of IoT devices and applications, a large number of devices that can sense and monitor the environment in IoT network are needed. This trend also brings great challenges, such as data explosion and energy insufficiency. This paper proposes a system that integrates mobile edge computing (MEC) technology and simultaneous wireless information and power transfer (SWIPT) technology to improve the service supply capability of WSN-assisted IoT applications. A novel optimization problem is formulated to minimize the total system energy consumption under the constraints of data transmission rate and transmitting power requirements by jointly considering power allocation, CPU frequency, offloading weight factor and energy harvest weight factor. Since the problem is non-convex, we propose a novel alternate group iteration optimization (AGIO) algorithm, which decomposes the original problem into three subproblems, and alternately optimizes each subproblem using the group interior point iterative algorithm. Numerical simulations validate that the energy consumption of our proposed design is much lower than the two benchmark algorithms. The relationship between system variables and energy consumption of the system is also discussed.

Magnetic State Generation using Hamiltonian Guided Variational Autoencoder with Spin Structure Stabilization

Numerical generation of physical states is essential to all scientific research fields. The role of a numerical generator is not limited to understanding experimental results; it can also be employed to predict or investigate characteristics of uncharted systems. A variational autoencoder model is devised and applied to a magnetic system to generate energetically stable magnetic states with low local deformation. The spin structure stabilization is made possible by taking the explicit magnetic Hamiltonian into account to minimize energy in the training process. A significant advantage of the model is that the generator can create a long-range ordered ground state of spin configuration by increasing the role of stabilization even if the ground states are not necessarily included in the training process. It is expected that the proposed Hamiltonian-guided generative model can bring about great advances in numerical approaches used in various scientific research fields.

A novel mutation in the cathepsin C (CTSC) gene in Iranian family with Papillon-Lefevre syndrome

Objectives

In this study, we analyzed the whole exomes of CTSC gene in a family with history of PLS.

Materials and methods

Genomic DNA was extracted from peripheral blood and genotype analysis was performed. The mutated protein sequence was used to find the best possible tertiary structure for homology modeling. The homology modeling of the novel mutation was then performed using the online Swiss‐Prot server. The results were also analyzed for to verify its validity.

Results

The analysis of CTSC gene elucidated a novel insertion GAC. The novel mutation was proved by analyzing 50 healthy control volunteers. Modeling of the novel found mutation in CTSC gene revealed structural defects that may have caused the functional abnormalities.

Conclusions

The structural analysis of the mutated protein model identifies changes in the stereo‐chemical and the energy level of the mutated protein. Since this protein play a role in the activation of granule serine proteases from cytotoxic T lymphocytes, natural killer cells, mast cells, such structural defects may lead to its malfunction causing dysfunctioning of immune defense mechanisms.

A comprehensive study on identifying the structural and functional SNPs of human neuronal membrane glycoprotein M6A (GPM6A)

Glycoprotein M6A, a stress related gene, plays an important role in synapse and filopodia formation. Filopodia formation is vital for development, immunity, angiogenesis, wound healing and metastasis. In this study, structural and functional analysis of high-risk SNPs associated with Glycoprotein M6-A were evaluated using six different bioinformatics tools. Results classified T210I, T134I, Y153H, I215T, F156L, T160I, I226T, R247W, R178C, W159R, N157S and P151L as deleterious mutants that are crucial for the structure and function of the protein causing malfunction of M6-a and ultimately leads to disease development. The three-dimensional structure of wild-type M6-a and mutant M6-a were also predicted. Furthermore, the effects of high risk substitutions were also analyzed with interaction with valproic acid. Based on structural models obtained, the binding pocket of ligand bound glycoprotein M6-A structure showed few core interacting residues which are different in the mutant models. Among all substitutions, F156L showed complete loss of binding pocket when interacting with valproic acid as compared to the wild type model. Up to the best of our knowledge this is the first comprehensive study where GPM6A mutations were analyzed. The mechanism of action of GPM6A is still not fully defined which limits the understanding of functional details encoding M6-A. Our results may help enlighten some molecular aspects underlying glycoprotein M6-A. Communicated by Ramaswamy H. Sarma.

Optimal Design of Hierarchical Cloud-Fog&Edge Computing Networks with Caching

This paper investigates the optimal design of a hierarchical cloud-fog&edge computing (FEC) network, which consists of three tiers, i.e., the cloud tier, the fog&edge tier, and the device tier. The device in the device tier processes its task via three computing modes, i.e., cache-assisted computing mode, cloud-assisted computing mode, and joint device-fog&edge computing mode. Specifically, the task corresponds to being completed via the content caching in the FEC tier, the computation offloading to the cloud tier, and the joint computing in the fog&edge and device tier, respectively. For such a system, an energy minimization problem is formulated by jointly optimizing the computing mode selection, the local computing ratio, the computation frequency, and the transmit power, while guaranteeing multiple system constraints, including the task completion deadline time, the achievable computation capability, and the achievable transmit power threshold. Since the problem is a mixed integer nonlinear programming problem, which is hard to solve with known standard methods, it is decomposed into three subproblems, and the optimal solution to each subproblem is derived. Then, an efficient optimal caching, cloud, and joint computing (CCJ) algorithm to solve the primary problem is proposed. Simulation results show that the system performance achieved by our proposed optimal design outperforms that achieved by the benchmark schemes. Moreover, the smaller the achievable transmit power threshold of the device, the more energy is saved. Besides, with the increment of the data size of the task, the lesser is the local computing ratio.

Minimizing Energy Consumption and Peak Power of Series Elastic Actuators: A Convex Optimization Framework for Elastic Element Design

Compared to rigid actuators, series elastic actuators (SEAs) offer a potential reduction of motor energy consumption and peak power, though these benefits are highly dependent on the design of the torque-elongation profile of the elastic element. In the case of linear springs, natural dynamics is a traditional method for this design, but it has two major limitations-arbitrary load trajectories are difficult or impossible to analyze and it does not consider actuator constraints. Parametric optimization is also a popular design method that addresses these limitations, but solutions are only optimal within the space of the parameters. To overcome these limitations, we propose a nonparametric convex optimization program for the design of the nonlinear elastic element that minimizes energy consumption and peak power for an arbitrary periodic reference trajectory. To obtain convexity, we introduce a convex approximation to the expression of peak power; energy consumption is shown to be convex without approximation. The combination of peak power and energy consumption in the cost function leads to a multiobjective convex optimization framework that comprises the main contribution of this paper. As a case study, we recover the elongation-torque profile of a cubic spring, given its natural oscillation as the reference load. We then design nonlinear SEAs for an ankle prosthesis that minimize energy consumption and peak power for different trajectories and extend the range of achievable tasks when subject to actuator constraints.

In silico Design of Laccase Thermostable Mutants From Lacc 6 of Pleurotus Ostreatus

Fungal laccase enzymes have a great biotechnological potential for bioremediation processes due to their ability to degrade compounds such as ρ-diphenol, aminophenols, polyphenols, polyamines, and aryldiamines. These enzymes have activity at different pH and temperature values, however, high temperatures can cause partial or total loss of enzymatic activity, so it is appropriate to do research to modify their secondary and/or tertiary structure to make them more resistant to extreme temperature conditions. In silico, a structure of the Lacc 6 enzyme of Pleurotus ostreatus was constructed using a laccase of Trametes versicolor as a template. From this structure, 16 mutants with possible resistance at high temperature due to ionic interactions, salt bridges and disulfide bonds were also obtained in silico. It was determined that 12 mutants called 4-DB, 3-DB, D233C-T310C, F468P, 3-SB, L132T, N79D, N372D, P203C, P203V, T147E, and W85F, presented the lowest thermodynamic energy. Based on the previous criterion and determining the least flexibility in the protein structures, three mutants (4-DB, 3-DB, and P203C) were selected, which may present high stability at high temperatures without affecting their active site. The obtained results allow the understanding of the molecular base that increase the structural stability of the enzyme Lacc 6 of Pleurotus ostreatus, achieving the in silico generation of mutants, which could have activity at high temperatures.

Conformational aspects of polymorphs and phases of 2-propyl-1H-benzimidazole

This paper reports on the polymorphism of 2-propyl-1H-benzimidazole (2PrBzIm) induced by temperature change. Upon heating, an irreversible reconstructive-type phase transition at T = 384 K from the ordered form I (P212121) to a new polymorph, form II HT (Pcam), was observed. The structural transformation between forms I and II involves significant changes in the crystal packing, as well as a key conformational variation around the propyl chain of the molecule. After the first irreversible phase transition, the II HT form undergoes two further (reversible) phase transitions upon cooling at 361 K (II RT) and 181 K (II LT). All three phases (forms II HT, II RT and II LT) have almost identical crystal packing and, given the reversibility of the conversions as a function of temperature, they are referred to as form II temperature phases. They differ, however, with respect to conformational variations around the propyl chain of 2PrBzIm. Energy calculations of the gas-phase conformational energy landscape of this compound about its flexible bonds allowed us to classify the observed conformational variations of all forms into changes and adjustments of conformers. This reveals that forms I and II are related by conformational change, and that two of the form II phases (HT and RT) are related by conformational adjustment, whilst the other two (RT and LT) are related by conformational change. We introduce the term 'conformational phases' for different crystal phases with almost identical packing but showing changes in conformation.

An Energy-Aware Runtime Management of Multi-Core Sensory Swarms

In sensory swarms, minimizing energy consumption under performance constraint is one of the key objectives. One possible approach to this problem is to monitor application workload that is subject to change at runtime, and to adjust system configuration adaptively to satisfy the performance goal. As today’s sensory swarms are usually implemented using multi-core processors with adjustable clock frequency, we propose to monitor the CPU workload periodically and adjust the task-to-core allocation or clock frequency in an energy-efficient way in response to the workload variations. In doing so, we present an online heuristic that determines the most energy-efficient adjustment that satisfies the performance requirement. The proposed method is based on a simple yet effective energy model that is built upon performance prediction using IPC (instructions per cycle) measured online and power equation derived empirically. The use of IPC accounts for memory intensities of a given workload, enabling the accurate prediction of execution time. Hence, the model allows us to rapidly and accurately estimate the effect of the two control knobs, clock frequency adjustment and core allocation. The experiments show that the proposed technique delivers considerable energy saving of up to 45%compared to the state-of-the-art multi-core energy management technique.

Electrostatic component of binding energy: Interpreting predictions from poisson-boltzmann equation and modeling protocols

Macromolecular interactions are essential for understanding numerous biological processes and are typically characterized by the binding free energy. Important component of the binding free energy is the electrostatics, which is frequently modeled via the solutions of the Poisson-Boltzmann Equations (PBE). However, numerous works have shown that the electrostatic component (ΔΔGelec ) of binding free energy is very sensitive to the parameters used and modeling protocol. This prompted some researchers to question the robustness of PBE in predicting ΔΔGelec . We argue that the sensitivity of the absolute ΔΔGelec calculated with PBE using different input parameters and definitions does not indicate PBE deficiency, rather this is what should be expected. We show how the apparent sensitivity should be interpreted in terms of the underlying changes in several numerous and physical parameters. We demonstrate that PBE approach is robust within each considered force field (CHARMM-27, AMBER-94, and OPLS-AA) once the corresponding structures are energy minimized. This observation holds despite of using two different molecular surface definitions, pointing again that PBE delivers consistent results within particular force field. The fact that PBE delivered ΔΔGelec values may differ if calculated with different modeling protocols is not a deficiency of PBE, but natural results of the differences of the force field parameters and potential functions for energy minimization. In addition, while the absolute ΔΔGelec values calculated with different force field differ, their ordering remains practically the same allowing for consistent ranking despite of the force field used. © 2016 Wiley Periodicals, Inc.

Capillary-induced deformations of a thin elastic sheet

We develop a three-dimensional model for capillary origami systems in which a rectangular plate has finite thickness, is allowed to stretch and undergoes small deflections. This latter constraint limits our description of the encapsulation process to its initial folding phase. We first simplify the resulting system of equations to two dimensions by assuming that the plate has infinite aspect ratio, which allows us to compare our approach to known two-dimensional capillary origami models for inextensible plates. Moreover, as this two-dimensional model is exactly solvable, we give an expression for its solution in terms of its parameters. We then turn to the full three-dimensional model in the limit of small drop volume and provide numerical simulations showing how the plate and the drop deform due to the effect of capillary forces.

Energy minimization in medical image analysis: Methodologies and applications

Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two categories: continuous method and discrete method. The former includes Newton-Raphson method, gradient descent method, conjugate gradient method, proximal gradient method, coordinate descent method, and genetic algorithm-based method, while the latter covers graph cuts method, belief propagation method, tree-reweighted message passing method, linear programming method, maximum margin learning method, simulated annealing method, and iterated conditional modes method. We also discuss the minimal surface method, primal-dual method, and the multi-objective optimization method. In addition, we review several comparative studies that evaluate the performance of different minimization techniques in terms of accuracy, efficiency, or complexity. These optimization techniques are widely used in many medical applications, for example, image segmentation, registration, reconstruction, motion tracking, and compressed sensing. We thus give an overview on those applications as well.

Snake-based brain white matter fiber reconstruction

Diffusion tensor imaging (DTI) is a tractography algorithm that provides the only means of mapping white matter fibers. Furthermore, because of its wealth of applications, diffusion MRI tractography is gaining importance in clinical and neuroscience research. This paper presents a novel brain white matter fiber reconstruction method based on the snake model by minimizing the energy function, which is composed of both external energy and internal energy. Internal energy represents the assembly of the interaction potential between connected segments, whereas external energy represents the differences between predicted DTI signals and measured DTI signals. Through comparing the proposed method with other tractography algorithms in the Fiber Cup test, the present method was shown to perform superiorly to the majority of the other methods. In fact, the proposed test performed the third best out of the ten available methods, which demonstrates that present method can accurately formulate the brain white matter fiber reconstruction.

Iterative graph cuts for image segmentation with a nonlinear statistical shape prior

Shape-based regularization has proven to be a useful method for delineating objects within noisy images where one has prior knowledge of the shape of the targeted object. When a collection of possible shapes is available, the specification of a shape prior using kernel density estimation is a natural technique. Unfortunately, energy functionals arising from kernel density estimation are of a form that makes them impossible to directly minimize using efficient optimization algorithms such as graph cuts. Our main contribution is to show how one may recast the energy functional into a form that is minimizable iteratively and efficiently using graph cuts.

Search of potential inhibitor against New Delhi metallo-beta-lactamase 1 from a series of antibacterial natural compounds

Background:

New Delhi metallo-β-lactamase-1 (NDM-1)-producing Gram-negative bacteria are today's major worldwide health concern. The enzyme NDM-1 provides bacterial resistance by its hydrolytic activity against the β-lactam ring of antibiotics. Inhibition of NDM-1 may prevent the hydrolysis of β-lactam ring of the antibiotics, and therefore, plays an important role against antibacterial resistance.

Materials and Methods:

Here we made an attempt to design suitable inhibitors against NDM-1 from different natural antibacterial compounds using molecular docking approach.

Results:

We observed that natural compounds such as Nimbolide and Isomargololone are showing an appreciable IC50 value as well as significant binding energy value for NDM-1. We further observed these compounds showing better affinity to NDM-1 on comparison with 14 β-lactam antibiotics.

Conclusion:

Finally, our study provides a platform for the development of a potent inhibitor of NDM-1, which may be considered as a potential drug candidate against bacterial resistance.

3Drefine: consistent protein structure refinement by optimizing hydrogen bonding network and atomic-level energy minimization

One of the major limitations of computational protein structure prediction is the deviation of predicted models from their experimentally derived true, native structures. The limitations often hinder the possibility of applying computational protein structure prediction methods in biochemical assignment and drug design that are very sensitive to structural details. Refinement of these low-resolution predicted models to high-resolution structures close to the native state, however, has proven to be extremely challenging. Thus, protein structure refinement remains a largely unsolved problem. Critical assessment of techniques for protein structure prediction (CASP) specifically indicated that most predictors participating in the refinement category still did not consistently improve model quality. Here, we propose a two-step refinement protocol, called 3Drefine, to consistently bring the initial model closer to the native structure. The first step is based on optimization of hydrogen bonding (HB) network and the second step applies atomic-level energy minimization on the optimized model using a composite physics and knowledge-based force fields. The approach has been evaluated on the CASP benchmark data and it exhibits consistent improvement over the initial structure in both global and local structural quality measures. 3Drefine method is also computationally inexpensive, consuming only few minutes of CPU time to refine a protein of typical length (300 residues). 3Drefine web server is freely available at http://sysbio.rnet.missouri.edu/3Drefine/.

Conformational energy studies of oxytocin and its cyclic moiety

Conformational energies were calculated for oxytocin in water, starting with a conformation proposed from nuclear magnetic resonance measurements in [U-(2)H](CH(3))(2)SO. Calculations on the isolated ring showed that conformations with one transannular hydrogen bond had the same energies as those without such bonds; those with two such hydrogen bonds do not appear to form. Calculations on the whole molecule also indicated the existence of several low-energy minima in the energy surface, and no preference for hydrogen-bond formation in the cyclic moiety; the hydrogen bond proposed between the Gly peptide NH and the Cys-6 C=O in the acyclic moiety can form. The proposed proximity of the tail to the ring is one of two low-energy conformations found. The Tyr side chain had two conformations of comparable energy, one over the ring between the Gln and Asn side chains, and the other with the Tyr side chain away from the ring. The oxytocin molecule appears to be flexible, and is probably sensitive to changes in its environment.