*2.4. Statistical Analysis*

Descriptive analyses were performed for all variables; in particular, categorical variables were expressed as frequency and proportion, whilst continuous variables were expressed as mean and standard deviation. To determine differences between groups in categorical variables, we used chi-square tests and Fisher's exact tests, as appropriate. After applying the Kolmogorov-Smirnov test and verifying the non-Gaussian distribution in most continuous variables, the differences between groups were evaluated using the Mann—Whitney U test. The reliability of the three standardized questionnaires was evaluated by assessing their internal consistency through the computation of Chronbach's alpha. Furthermore, in order to identify possible predictors of outcomes considered in the current investigation, we adopted different models: we used the generalized linear models for EQ-5D-Index, for EQ-VAS, and for each one of the 14 coping strategies of Brief-COPE; in addition, we estimated univariate and multivariate logistic regression models for Athens Insomnia Scale (dichotomized variable in according to previously described criterion). *p* values < 0.05 were considered statistically significant and reported in bold characters in the Tables. Statistical analysis was performed using IBM SPSS Statistics 23 (IBM Corp, Armonk, NY, USA).


**8.8. Source group 8—miscellaneous**

This source group has classifiedinto five categories. The source categories listed on toolkit guide are: drying of biomass, crematoria, smokehouses, dry cleaning, and tobacco smoking.

### **8.9. Source group 9—disposal**

Waste disposal is an anthropogenic activity which contributes to the emission of the unintentional persistence organic pollutant so-called polychlorinated dibenzodioxins and polychlorinated dibenzofuran (PCCD/PCDF). Under this source group, landfills, waste dumps and- landfill mining, sewage/sewage treatment, open water dumping composting, and waste oil disposal are source categories which have major contribution for emission of dioxin and furan [2].


**11.6 Liposome as anti-infective agents**

Amphotericin B (ambisome) is now available in liposome based formulation has passed all the clinical trials. Liposomal Amphotericin B targets liver and spleen by passively and reduces renal toxicity at normal dose and toxicity appears back when given at higher dose [110, 111].

#### **11.7 Liposome in tumor therapy**

Long therapy treatment with anticancer drugs gives several toxic effects but liposomal for tumor cells showed less side effects. Reported methods showed liposomes targets to tumor cells and circulate for longer time period with enhanced vascular permeability [112, 113]. In the year 1995, Doxil which is Doxorubicin PEGylated liposomes, for intravenous administration prepared by stealth technology was approved for hematological tumors [5, 6]. Caelyx and myocet are other liposome preparations for same doxorubicin used for advanced breast cancer [114–116].


**Industrial Wireless Sensor Networks: Protocols and Applications**

Editors

**Seong-eun Yoo Taehong Kim**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Seong-eun Yoo Daegu University Korea

Taehong Kim Chungbuk National University Korea

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Sensors* (ISSN 1424-8220) (available at: https://www.mdpi.com/journal/sensors/special issues/IWSNPA).

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**6. Interaction of molecules with DNA**

196 Bioinformatics

2004].

**5. Selective formation of complexes**

ligands with this purpose [Kaye, 2011].

31G\*\* calculation of atomic charges [Sakakura et al., 2011].

There are several examples of molecular modelling studies on complexes between cyclic receptors and ammonium ions, calixarenes [Choe & Chang, 2002] and crown ethers being the most used. As an example, it is noteworthy to mention the theoretical studies on calix[4]crown-5 and a series of alkyl ammonium ions [Park et al., 2007], having shown that the energy of complex formation depends on the number of amine groups in the alkyl chain as well as on the number of methylene groups between the primary and secondary amine groups, results that agree with experimental measurements. Although the calculations are performed under quite different conditions of vacuum compared with the experimental conditions of the phase system of chloroform-water, the binding properties of calixarenetype compounds towards alkyl ammonium ions have been successfully simulated,

providing general and useful explanations for the molecular recognition behaviour.

Complex formation of compounds containing benzene rings with ammonium cations has also been theoretically studied using many computational techniques, including *ab initio* calculations [Kim et al., 2000]. It has been shown that two types of NH-aromatic π and CHaromatic π interactions, which are important in biological systems, are responsible for binding, and that charged hydrogen bonds versus cation-π interaction is the origin of the high affinity and selectivity of novel receptors for NH4+ over K+ ions [Oh et al., 2000]. Organic molecules complexed with metal cations have also been studied by MM2 molecular modelling [Mishra, 2010]. The search for metal ion selectivity is of interest in the field of biomimetic models of metalloenzymes and molecular modelling helps in the design of new

Molecular modelling has been used to suggest possible contributions of carrier effectivity and selectivity to complex formation in accordance with experimental results [Chipot et al., 1996; Ilioudis et al., 2005]. Our research group has evaluated the possible cation-receptor interactions involved in the complexes with ammonium and metal cations of selective carriers using the Amber force field with appropriate parameters developed by us. The complexation energies obtained are in reasonable agreement with experimental values, taking into account that complexation/decomplexation processes have a great influence on transport rates and are not equally favoured in cyclic and acyclic carriers [Campayo et al.,

Both binding and selectivity in binding can be understood through the combined efforts of several non-covalent interactions, such as hydrogen bonding, electrostatic interactions, hydrophobic interactions, cation-π interactions, π-π stacking interactions and steric complementarity [Späth & König, 2010]. Formation of complexes is also possible in the case of neutral ligands. For example, the interactions between cholesterol and cyclodextrins have been theoretically studied to investigate their 1:1 and 1:2 complexes [Castagne et al., 2010], while the formation of stable complexes between trehalose and benzene compounds have been investigated by the general Amber force field (GAFF) and Gaussian 03 for MP2/6G-

Anthracycline antibiotics such as doxorubicin and its analogues have been in common use as anticancer drugs for around half a century. There has been intense interest in the DNAbinding sequence specificity of these compounds in recent years, with the hope of identifying a compound that can modulate gene expression or exhibit reduced toxicity. Cashman and Kellog have studied models of binding for doxorubicin and derivatives [Cashman & Kellog, 2004], looking for sequence specificity and the effects of adding aromatic or aliphatic ring substituents or additional amino or hydroxyl groups. They performed a hydropathic interaction analysis using the HINT program (a Sybyl program module, Tripos Inc.) and four double base pair combinations. Interaction of some intercalators with two double DNA base pairs have also been studied with the density functional based tight binding (DFTB) method [Riahi et al., 2010], despite DFT methods being known to be inherently deficient in calculating stacking interactions, and the Amber force field and then AM1 to dock the intercalator between DNA base pairs [Miri et al., 2004].

Studies on sequence-selectivity of DNA minor groove binding ligands have shown that the most reliable results for AT-rich DNA sequences are obtained when MD simulations are performed in explicit solvent, when the data are processed using the MM-PB/SA approach, and when normal mode analysis is used to estimate configurational entropy changes [Shaikh et al., 2004; Wang & Laughton, 2009]. Use of the GB/SE model with a suitable choice of parameters adequately reproduces the structural and dynamic characteristics in explicitly solvated simulations in approximately a quarter of the computational time, although limitations become apparent when the thermodynamic properties are evaluated [Sands & Laughton, 2004]. Water molecules taking part in the complexation have been studied using

the MMX force field and then PMB for gas phase optimisation, followed by re-optimisation in aqueous phase with the PM3 method using the AMSOL package [Silva & Jayasundera, 2002]. Optimisation geometries with AM1 and the use of implicit solvent have been taken into account when considering intercalation versus insertion into the minor or major groove [Bendic & Volanschi, 2006].

Using Molecular Modelling to Study Interactions Between Molecules with Biological Activity 199

ΔU = EinterES + ΔES + ΔEE + EdesolvS + EdesolvE (3)

estimate ligand binding affinities and selectivities, with various levels of accuracy and computational expense: free energy perturbation (FEP), thermodynamic integration (TI), lineal response (LR), and molecular mechanics Poisson-Boltzman surface area (MM/PBSA). Identification of conformational preferences and binding site residues, as well as structural and energetic characterisation, is possible using MD simulations [Anzini et al., 2011; Dastidor et al., 2008; Stoika et al., 2008]. It is also possible to estimate conformational energy penalties for adopting the bioactive conformation identified by using a pharmacophore

A model based on van der Waals intermolecular contribution from Amber and electrostatic interactions derived from the Poisson-Boltzman equation has been used to predict the change in the apparent dissociation constant for a series of six enzyme-substrate complexes during COMBINE analysis [Kmunicek et al., 2001]. In COMBINE analysis, binding energies are calculated for the set of enzyme-substrate complexes using the molecular mechanics force field. The total binding energy, ΔU, may be assumed to be the sum of five terms: the intermolecular interaction energies between the substrate and each enzyme residue, EinterES, the change in the intramolecular energy of the substrate upon binding to the enzyme, ΔES, the change in the intramolecular energy of the enzyme upon binding, ΔEE, the desolvation

When the substrate is a rather small molecule, there is no evidence for large differences in the structure of the enzyme when different substrates are bound and so the second and third are neglected. This method identifies the amino acid residues responsible for modulating

Molecular modelling of proteins is sometimes directed towards homology modelling, enabling progress in understanding the mechanisms of action despite the lack of detailed information on the 3D structure of a protein. Molecular dynamic simulations are usually used to test the stability of the complete structure derived from homology modelling

Molecular docking examples can be used to compare relative stabilities of the complexes, but not calculate binding affinities, since changes in entropy and solvation effects are not taken into account [Pastorin et al., 2006; Tschammer et al., 2011]. In any case, docking calculations are common studies on novel drugs, Autodock being one of the most used docking programs [see for example Venskutonyte et al., 2011]. Docking programs treat enzymes and substrates as rigid entities, but flexible docking is also possible, if several different protein conformations extracted from molecular dynamic simulations are used

In our laboratory, molecular modelling has been tentatively used to study the trypanosomicidal activity of some phthalazine derivatives. Results obtained with Amber force field implemented in HyperChem 8.0 plus our own necessary parameters, and with AutoDock 4.2 using the PDB structure for *T. cruzi* Fe-SOD enzyme, were in accordance with

energy of the substrate, EdesolvS, and the desolvation energy of the enzyme, EdesolvE.

model [Frølund et al., 2005].

enzyme activity [Kmunicek et al., 2005].

[Srinivas et al., 2006].

[Roumen et al., 2010].

Calculating the curvature radius of molecular DNA structures has been reported [Slickers et al., 1998] as a new method for understanding the dependence of binding affinity on ligand structure, assuming that strong binders should have a shape complementary to the DNA minor groove. A method for predicting sequence selectivity and minor groove binding, based on MD simulations on DNA sequences with and without the bound ligand, to obtain an approximate free energy of binding has been proposed [Wang & Laughton, 2010].

Amber force field, developing the necessary parameters, has also been used together with electrostatic potential-derived (ESP) charges and explicit solvent molecules to study bisintercalation into DNA. The targeted molecular dynamics (tMD) approach has been considered for comparing the relative energetic cost involved in creating the intercalation sites and also studying the mechanisms of action [Braña et al., 2004]. It has been found that the electrostatic contribution is a critical characteristic of binding selectivity [Marco et al., 2005]. Reports on duplex and triplex formation of oligonucleotides by stacking aromatic moieties in the major groove, using Amber force field and the GB/SA solvation model in molecular dynamic simulations, can be found in the literature [Andersen et al., 2011]. Studies on docking using GOLD [Kiselev et al., 2010] to optimise the starting structures with the MMFF94 force field have also been performed.

Most of the published molecular modelling studies use two double base pairs or more than eight double base pairs to represent DNA. In our opinion, molecular modelling of DNA intercalation complexes should be done using at least the two base pairs of the intercalation site and an additional base pair at the two strand ends to maintain DNA shape and avoid distortion leading to inaccurate results. That means four base pairs for monointercalation studies and five or six base pairs for bisintercalation ones should be used. Using these DNA models, our studies on the mono and bisintercalation of benzo[*g*]phthalazine derivatives strongly suggest the possibility of bisintercalation and the important role played by an Nmethyl group in stabilising the DNA complex of one of the compounds, throwing some light over the experimental results obtained [Rodríguez-Ciria et al., 2003]. The possibility of bisintercalation for a 1,4-disubstituted piperazine has been studied on duplexes of five and six base pairs, obtaining much better results in the case of five base pairs, in accordance with the theoretical calculations of binding mode not conforming to the neighbouring exclusion principle proposed by different authors [Veal et al., 1990].


**3. Conclusions**

Previous physiological, ecological and biochemical studies [4] as well as genomic analyses [5], indicate that the anoxygenic phototroph bacterium *Cfl. aurantiacus* has many interesting and certain unique features in its metabolic pathways. The evolutionary links of *Cfl. aurantiacus* and other phototrophic bacteria suggested from this report are summarized in **Table 1.** It has been recognized that the type II RCs were transferred between the *Chloroflexi* species (or FAPs) and the anoxygenic phototrophic Proteobacteria. Sequence alignments and phylogenetic analyses illustrated in this report suggest: (i) Some *Cfl. aurantiacus* enzymes in essential metabolic pathways are more related to the anoxygenic phototrophic Proteobacteria than other phototrophic bacteria, whereas other enzymes are more related to other phototrophic bacteria than anoxygenic phototrophic Proteobacteria; and (ii) some *Cfl. aurantiacus* enzymes in essential carbon metabolic pathways are more related to nonphotosynthetic microbes than other phyla of phototrophic bacteria. Together, our studies support lateral/horizontal gene transfers among microbes, and suggest that photosynthesis is likely an adaption to the environments [9].

a The *Cfl. aurantiacus* complex I with clustered genes (**Fig. 2A**) b The *Cfl. aurantiacus* complex I without clustered genes (**Fig. 2B**) c The putative bchE genes in some cyanobacteria have been reported not to function as anaerobic cyclase.

Table 1. Evolutionary perspectives of selective proteins/enzymes/cellular complexes in *Cfl. aurantiacus* versus other phyla of phototrophic bacteria


**About the Editors**

**David W. Inglis** is Associate Professor in the School of Engineering at Macquarie University (Sydney Australia), where he is the leader of the Biomedical Microdevices Group. Dr. Inglis received his B.Sc. in Engineering Physics from the University of Alberta in 2001 and a Ph.D. in Electronic Materials and Devices from Princeton University in 2007. He is an expert in microfluidic particle separation and has published more than 40 journal articles which have been cited more than 2500 times in total.

**Majid Ebrahimi Warkiani** is Associate Professor in the School of Biomedical Engineering at UTS, Sydney, Australia. He received his Ph.D. in Mechanical Engineering from Nanyang Technological University (NTU) under the prestigious SINGA scholarship from A\*STAR and undertook postdoctoral training at Massachusetts Institute of Technology (SMART Centre). He is an NHMRC-CD fellow and also a member of the Institute for Biomedical Materials & Devices (IBMD) and Center for Health Technologies (CHT) at UTS.

Dr Warkiani's current research activities focus on three key areas of (i) microfluidics involving the design and development of novel microfluidic systems for particle and cell sorting (e.g., circulating tumor cells, fetal cells, and stem cells) for diagnostic and therapeutic applications; (ii) organ-on-a-chip involving the fabrication and characterization of novel 3D lab-on-a-chip systems (e.g., lung-on-a-chip, tumor-on-a-chip) to model physiological functions of tissues and organs; and (iii) 3D microprinting involving the design and development of novel miniaturized systems (e.g., micromixers, microcyclones) for basic and applied research.

**Mohammad A. Qasaimeh** is Assistant Professor of Mechanical and Biomedical Engineering at New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE, and with the Mechanical and Aerospace Engineering Department at Tandon School of Engineering, New York University (NYU), New York, USA. He established the Advanced Microfluidics and Microdevices Laboratory (AMMLab) in 2014, and his current research interests include developing microfluidic and MEMS devices for clinical applications and point-of-care diagnostics. Recently, Dr. Qasaimeh was awarded the Technology Innovation Pioneers (TIP) Award during the TIP 2020 Summit. Prior to joining NYUAD, he was a Postdoctoral Research Associate at Massachusetts Institute of Technology and a Research Fellow at Harvard Medical School. Dr. Qasaimeh completed his Ph.D. degree in Biomedical Engineering at McGill University, where he received several prestigious fellowships and awards including the NSERC Postdoctoral Fellowship, the Alexander Graham Bell Graduate Scholarship, and the FQRNT Researchers Stars Award. Dr. Qasaimeh's research has been published in many peer-reviewed journals including *Nature Communications*, *Advanced Biosystems*, *Lab on a Chip*, *iScience*, *Advanced Therapeutics*, and *Scientific Reports*. He delivered more than 30 keynote and invited lectures at national and international conferences and is actively involved in organizing several local and international conferences. Currently, he is serving as a Co-Chair at the NYU Biomedical and Biosystems Conference series and as a Program Chair of the International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS). Dr. Qasaimeh is serving as an Associate Editor with the *IEEE Nanotechnology Magazine*, a Topic Editor with the journal *Biosensors*, a Review Editor with the journal *Frontiers in Bioengineering and Biotechnology*, and an Editorial Board Member of *Scientific Reports* of the Nature Publishing Group.

**Weiqiang Chen** is Associate Professor in the Departments of Mechanical and Aerospace Engineering and Biomedical Engineering at New York University. He is the recipient of the Biomedical Engineering Society Young Innovator Award of Cellular and Molecular Bioengineering (2019), the Chroma Young Investigator Award in Biomedical Engineering (2019), the Lab on a Chip Emerging Investigator Award (2018), the National Institute of Biomedical Imaging and Bioengineering Trailblazer Award (2018), the NYU Whitehead Fellowship in Biomedical and Biological Sciences (2017), the Goddard Junior Faculty Award (2017), the American Heart Association Scientist Development Award (2016), and the Baxter Young Investigator Award (2013). Dr. Chen's research interests are focused on lab-on-a-chip, biosensing, cell mechanobiology, stem cell biology, cancer biology, and immune engineering.