Since the success of monolayer graphene exfoliation, two-dimensional (2D) materials have been extensively studied due to their unique structures and unprecedented properties. Among these fascinating studies, the most predominant focus has been on their atomic structures, defects, and mechanical behaviors and properties, which serve as the basis for the practical applications of 2D materials. In this review, we first highlight the atomic structures of various 2D materials and the structural and energy features of some common defects. We then summarize the recent advances made in experimental, computational, and theoretical studies on the mechanical properties and behaviors of 2D materials. We mainly emphasized the underlying deformation and fracture mechanisms and the influences of various defects on mechanical behaviors and properties, which boost the emergence and development of topological design and defect engineering. We also further introduce the piezoelectric and flexoelectric behaviors of specific 2D materials to address the coupling between mechanical and electronic properties in 2D materials and the interactions between 2D crystals and substrates or between different 2D monolayers in heterostructures. Finally, we provide a perspective and outlook for future studies on the mechanical behaviors and properties of 2D materials. For MThe common types of 2D oxides include lead, phosphorus, and transition metal oxides a–d 82,8,882,83. The imperfections introduced during production processes inevitably distort the pristine lattices and have a significant influence on the mechanical properties of materials. The accurate manipulation of defects has been recognized as an effective approach to alter/modify the mechanical properties of materials. Structural defects in 2D materials have attracted much attention since the era of graphene has arisen. In particular, design based on topological defects in 2D materials has become a hot topic in the field of mechanics and material sciences ,86. HoweAs shown in 1 (5-9) defect), and one dangling bond [2 (5-8-5) defect] arrangement, with a formation energy of 8.7 eV . . 2, but nalities . The GBsGB kinks c,d [107,GB kinks b and hasn the GB .2 mentioned above, the formation energies of vacancies in phosphorene are much lower, which is attributed to the weaker P–P bond and the puckered structure [Noncarbon 2D materials with a single element have also attracted tremendous attention due to their unique structures and distinct properties. The phosphorus monolayer exfoliated recently exhibited fascinating electronic properties for future applications ,113,114.tructure .2 molecule first dissociates and then reacts with P atoms in phosphorene [Interstitial defects usually appear during the growth of 2D phosphorus sheets. The self-interstitial P atom prefers to form two bonds with P atoms in different layers, leaving one dangling bond . As a resphorene ,119. FirAlthough there are many allotropes for boron, the boron monolayer on the silver surface under ultrahigh vacuum conditions was successfully synthesized in 2015 . Boropenv) have been recently discovered from both theoretical predictions [v1/9 structure is predicted to be the most stable [Unlike other 2D crystals, multiple polymorphs of borophene with different arrangements of periodic hexagonal holes  found in borophene structures have similar lattices and can serve as the building blocks of a number of borophene crystalline phases by controlling the mixing ratio of the two rows  nanoindentation by Lee et al. . As showe strips ,133,134.e strips . Table 12 and graphene nanoribbons [2 is consistent with that from DFT calculations, while the measured strength varies within a large range. Such considerable variation is attributed to undetectable pre-existing cracks introduced during the synthesis process [Although AFM nanoindentation has been widely adopted to measure the elastic properties and strength of 2D materials, it induces highly localized stress and strain underneath the AFM tip, which to some extent affects the strength measurement. A nanodevice was recently developed to achieve uniform tensile stress and used for mechanical testing of MoSeoribbons . The obt process . Current2 [2 are isotropic, as indicated by Atomistic simulation is an increasingly powerful tool for investigating the mechanical properties of nanostructured materials . As an a2 . As an iTo describe the elastic properties of 2D materials, a molecular mechanics model and molecular structural mechanics model have been developed as a bridge to link atomic structures with macroscopic properties. Aiming at the linear elastic properties (such as Young’s modulus and Poisson’s ratio), Chang and Gao first adopted an effective stick-spiral model and derived analytical expressions to describe the elastic moduli of graphene and carbon nanotubes . Beyond Defects have a significant influence on the mechanical properties of 2D materials. Even the mechanical properties of 2D materials can be tuned by controlling the density or distribution of defects. As mentioned above, vacancies are one of the most common structural defects in 2D materials. Interestingly, a controlled density of vacancies introduced in graphene by irradiation can significantly increase the Young’s modulus up to 550 N/m . This phSingle crystalline graphene is usually obtained from mechanical exfoliation and has a limited in-plane length scale. In recent years, the chemical vapor deposition (CVD) growth approach has been used as a main synthesis method for the large-scale production of graphene. However, the CVD-grown graphene involves some GBs. Understanding the GB effects on the mechanical properties is essential for the applications of graphene. Recent nanoindentation tests have confirmed that the Young’s modulus and strength of CVD-grown high-quality graphene are comparable to pristine graphene . NotablyThe size effects of strength have been widely investigated in various nanostructured materials, for example, the classic Hall–Petch relationship in polycrystalline metals. Analogously, substantial attention has also been drawn to polycrystalline graphene ,179,180.The MD simulations mentioned above have revealed that nanocrystalline failure during tension typically initiates at GB junctions. The grain size effect of the strength largely depends on the detailed microstructures of the GBs. To date, nearly all present studies on the mechanical properties of polycrystalline materials are based on MD simulations, but physically realistic GB arrangements might have a certain discrepancy with the constructed samples used in modeling. Therefore, real experiments are still needed to investigate the relationship between polycrystalline graphene strength and grain size. Theoretical models of realistic GB structures are also required to further study the mechanical properties and behaviors of polycrystalline graphene.2 [The theoretical strength describes the maximum stress that can be sustained by perfect materials, while the fracture toughness is a measure of the material’s resistance to crack prorogation. Since crack-like flaws are inevitably introduced to 2D materials during synthesis or transfer, the fracture toughness of 2D is essential for practical applications of 2D materials . Recentl2 , which i2 ,184. The2 . The cra2 ,186. MD 2 . Such a 2 .2, respectively [The fracture behaviors and properties of polycrystalline graphene have been recently investigated via in situ experiments and atomistic simulations ,182,188.ectively ,182,188.ectively ,188. As ectively . Such inectively . The cri2 have shown that MoS2 has a much lower critical stress intensity factor than graphene [2 is dependent on the orientation. Furthermore, the crack edge chirality is found to determine the crack propagation path because of the more complex atomic arrangement of MoS2. The zigzag crack prefers a straight path, while the armchair crack extends via a kink, leaving a zigzag edge [2. However, MoSe2 is too brittle to sustain precracks through FIB cutting, resulting in a catastrophic failure during the cutting process. The lower surface energy of MoSe2 further suggests that MoSe2 is a more brittle material than graphene [With the bursting of various 2D materials, their fracture behaviors have drawn increasing attention. MD simulations of the fracture of perfect MoSgraphene . Similarzag edge . For phozag edge . Due to zag edge . The samgraphene .It is a long-standing challenge in the field of material science to achieve a material with both high strength and high fracture toughness. As a representative 2D material, although pristine graphene has very high stiffness and strength, it possesses a rather low fracture toughness. As crack-like flaws are inevitable in graphene, an effective toughening strategy is required to improve graphene’s toughness and ensure its safety and reliability during applications. The introduction of controlled topological defects has been proven to be an effective solution to toughness enhancement. It has been demonstrated that topological defects (such as disclinations and dislocations) give rise to apparent out-of-plane wrinkling, which can be utilized to generate 3D graphene structures and further alter the fracture properties . Using t2 kirigami structure [Another promising toughening method utilizes the interaction between nanocracks, i.e., establishing the concept of kirigami design. Recent MD simulations showed that patterning graphene with cuts results in a significant increase in yield and fracture strains ,198. Thetructure . As a retructure ,201,202.tructure . Recentltructure . Althougtructure , kirigamNanocomposites are also a significant toughening strategy for 2D materials. One-dimensional carbon nanotubes (CNTs) are embedded in graphene to construct a hybrid structure, figuratively called “rebar-graphene.” Rebar graphene is demonstrated to remarkably enhance the fracture toughness of pristine graphene h,i. Such2 also suffers out-of-plane ripples when subjected to shear loading. In particular, under the mixed mode of modes I and II, buckling cracks are generated due to out-of-plane deformation [The 2D materials exhibit other fracture modes (such as shear and tear failure) except mode I fracture. Due to the single-atom-layer structure, graphene under shear exhibits out-of-plane wrinkles, further affecting the in-plane mechanical properties. MD simulations have shown a reduction in fracture strength due to shear-induced ripples . The forormation . The proormation .Tearing is also a significant fracture mode that occurs during the fabrication and applications of 2D materials . Moura a9 cycles at an average stress of 71 GPa and a stress range of 5.6 GPa [Recently, Cui et al. used AFM to investigate the fatigue behaviors of graphene and graphene oxide . It was  5.6 GPa . These f 5.6 GPa . Cui et  5.6 GPa . The fat 5.6 GPa . These s2 monolayer, oscillating piezoelectric outputs were detected under cyclic loadings  is considered an ideal candidate because of its natural presence of triangular nanopores [3N4 to be piezoelectric. More importantly, such piezoelectricity does not vanish in structures with multilayers, which is convenient for its fabrication and applications [Another intriguing approach to create in-plane flexoelectricity is the introduction of noncentrosymmetric nanopores. Tight-binding modeling and DFT calculations have been adopted to investigate graphene monolayers containing triangular or trapezoidal pores g 236,23. Due to anopores . Both DFications .Bulk materials with laminate structures are widely used as solid lubricants because of interlayer weak bonding by van der Waals forces. The high relative surface area and good thermal properties of 2D materials make them ideal candidates for lubricants in nanoscale electronic or mechanical systems . The AFMThe friction measurements via frictional force microscopy (FFM) of the chemically modified graphite showed an unexpected increase with decreasing normal load, indicating a negative friction coefficient . The cor2D materials generally exist in the forms of single layers, few layers, or multiple layers. It is necessary to investigate the interlayer shearing and sliding of 2D materials to further explore their assembly and to ensure their performance and reliability during applications. When two graphite sheets are in incommensurate contact, they can easily slide with each other, exhibiting superlubricity due to the ultralow shear strength between two graphite sheets . Such suThe defects of 2D materials alter the surface states of the structure, leading to apparent modifications of their friction behaviors. FFM measurements showed that charged iron irradiation-induced defects in single-layer graphene efficiently enhanced friction . MD simuA wide range of applications of graphene results in complicated environmental exposure. When exposed to humid conditions, graphene was observed to present increased friction compared with that in a dry atmosphere, which becomes more apparent with aging. Experiments and simulations have indicated that increasing friction is associated with variations in surface energy . FurtherWhen 2D materials are attached to substrates, the interaction strength between these systems has proven to be a determinant of their friction behaviors . ModificThe booming study of 2D materials has attracted much attention beyond the simple structures and properties of graphene and its analogs. Indeed, the interactions between 2D materials and substrates and between heterogeneous multilayers have attracted enormous interest due to their distinct properties and exciting applications.Strain engineering of 2D materials has brought a new dawn of fundamental mechanisms and thrilling applications. Graphene, although it exhibits extraordinary electronic properties, lacks a band gap, making it intricate in practical applications. Various approaches have been proposed to modify the band gap in graphene to broaden its applications, among which the mechanical strain method has drawn much attention. First-principle calculations have indicated that the opening of the band gap is expected through asymmetrical strain. When uniaxial tension is applied to graphene, a tunable band gap of graphene can be achieved, but the symmetrical biaxial strain has a negligible influence on the band gap opening ,259. The2 under uniaxial strain have been indirectly observed [Due to the atomic nature of 2D materials, precise strain control over monolayer or few-layer structures remains challenging. Currently, the strain engineering of 2D materials can be achieved by epitaxial growth, thermal strain, and flexible substrates . The epiobserved ,264. The2-supported graphene presents pronounced weakened carrier mobility, which results from the surface roughness and interlayer contamination. Graphene laid on a single crystalline h-BN has demonstrated a remarkable enhancement in carrier mobility compared with that on SiO2 substrates [2 sandwiched with graphene may enlighten further development [The evolution of research on graphene and isolated 2D crystals has shifted focus to atomic layers vertically stacked by different 2D materials. Van der Waals-bonded heterostructures are emerging with unique structural and electronic varieties, resulting in their possible applications in novel electronic and photoelectronic devices ,266. Silbstrates . This substrates . The hetbstrates . The comelopment .2/MoSe2 [2/WSe2 [The tunable electronic and optoelectronic properties of heterostructures can be achieved through the crystallographic orientation between stacking crystals. For the graphene on the h-BN substrate, the similar hexagonal structure with a 1.8% difference in lattice constant results in a moiré structure when joining. The induced periodic potential forms a secondary Dirac point . Rotatio2/MoSe2  and MoS2 [2/WSe2 . However [2/WSe2 ,275. Des [2/WSe2 ,275.In this review, we summarize recent advances in experimental, computational, and theoretical studies on the mechanical behaviors and properties of various 2D materials, including their tension and fracture behaviors, piezoelectricity, flexoelectricity, friction, interlayer shear properties, and van der Waals interactions. We further emphasize that some common defects in 2D materials have a profound influence on their mechanical behaviors, suggesting tunable properties by defect engineering. Although graphene discovery has stimulated prosperous studies of 2D materials, there are still open questions calling for further exploration.2, or h-BN monolayers, while experiments have seldom been conducted for other newly synthesized 2D crystals due to technological limitations and difficulties. The extraordinary properties of novel 2D crystals largely remain as theoretical proposals. In addition, the experimental approach is quite limited for mechanical measurements of 2D materials. The pioneering work of nanoindentation tests for graphene has become the most frequent mechanical characterization approach for 2D materials. However, nanoindentation induces local deformation and only reveals an average property for anisotropic 2D materials. A more effective analytical model is needed for extracting the intrinsic mechanical properties of 2D materials [Numerous experimental characterizations and mechanical tests have been mainly performed on graphene, MoSaterials ,277. ForThe defect engineering or topological design of 2D materials has drawn much attention for tunable mechanical or electronic properties, but there is a general lack of fundamental understanding of intrinsic mechanisms for defective structures. For the mechanical strength, the disagreement of various defects’ influences calls for experimental verifications and further theoretical explanations. The toughening of brittle 2D crystals has been realized via topological design. It is then necessary to further probe the optimization of fracture properties by controlling patterned defects, which may be a heavy burden due to variant design parameters. Further explorations of tailoring fracture behaviors rely mostly on understanding toughening mechanisms and interactions between cracks and defects, which until now remain mysterious. Although the introduction of patterned defects into pristine 2D crystals has been given by previous studies ,279, theInvestigations on 2D materials have extended from specific performances to taking them as platforms for heterostructure studies. In addition, unstable 2D structures require encapsulation by other 2D materials with chemical inertness for practical applications. The typical assembly process of heterostructures contains the layer-by-layer transfer of monolayer 2D crystals. This procedure has high inefficiency, which urges the further development of assembly techniques. The effort of epitaxial growth on other 2D materials as templates endows the possibility of large-scale synthesis for industrial applications ,281. RecA new era of nanotechnology has been seen since the first discovery of graphene monolayers. After extensive efforts in the scientific community, 2D materials have been revealed as marvelous candidates for many applications, from conventional mechanics, electronics, and optics to novel biomedicine, drug delivery, and energy storage. The emergence of vertically stacking heterostructures has broadened the studies of 2D materials beyond monolayer crystals to multilayer structures by human design. An expanding number of family members and defect engineering methods further provide multiple choices for tailoring the mechanical, physical, and chemical properties of 2D material systems, which can be used for future practical applications.


We review the evidence for bio-regulation by coral reefs of local climate through stress-induced emissions of aerosol precursors, such as dimethylsulfide. This is an issue that goes to the core of the coral ecosystem’s ability to maintain homeostasis in the face of increasing climate change impacts and other anthropogenic pressures. We examine this through an analysis of data on aerosol emissions by corals of the Great Barrier Reef, Australia. We focus on the relationship with local stressors, such as surface irradiance levels and sea surface temperature, both before and after notable coral bleaching events. We conclude that coral reefs may be able to regulate their exposure to environmental stressors through modification of the optical properties of the atmosphere, however this ability may be impaired as climate change intensifies. Coral reefs cover some 600,000 square kilometers of the earth’s surface (0.17% of the ocean surface), with coral ecosystems amongst the most diverse on the planet . Coral rOn many reefs, reduced stocks of herbivorous fishes together with increased sediment and nutrient loading from land-based activities  have cauSince the late 20th century coral reefs have been subjected to a new range of environmental threats associated with climate change that can seriously jeopardize their continued existence unless radical change occurs in the governance and management of reef systems . These tInterestingly, reduced incoming light due to cloudy conditions has been shown to mitigate bleaching in the Pacific . Coral rOver the past 10–15 years, field and laboratory studies have provided evidence for the existence of a hitherto unrecognized climate bio-regulation process in coral reefs . This isOur understanding of aerosol-climate interactions although growing, is still incomplete, being identified by the Intergovernmental Panel on Climate Change (IPCC) as one of the key sources of uncertainty in our knowledge of Earth’s energy budget and anthropogenic climate forcing . This isThe quite separate fields of coralline ecology and aerosol-climate interactions both have a long and rich history. Unsurprisingly however, given the distinct disciplines involved, the intersection of these fields is relatively recent. Indeed, most of the published research on biogenic aerosol emissions by coral reefs has appeared in the last two decades. Notwithstanding the emerging nature of the field, there are numerous aspects of the topic that bridge the disciplines of climatology, aerosol science and coral reef ecology. Consequently, we have chosen to employ an integrative or critical review approach. Our aim is to assess the current evidence for coral reef bio-regulation of climate and to synthesize the literature in a way that will enable a new theoretical framework and paradigm to emerge , 2016. LAerosols are minute solid or liquid particles suspended in the atmosphere and are derived from a variety of natural and anthropogenic sources, ranging from industrial processes, volcanic eruptions, biomass burning and marine ecological processes. Aerosol particles are either emitted directly to the atmosphere (primary aerosols) or produced in the atmosphere from precursor gases (secondary aerosol). All atmospheric aerosols scatter incoming solar radiation, and a few aerosol types  can also absorb solar radiation. Aerosols that mainly scatter solar radiation have a cooling effect, by enhancing the total reflected solar radiation from the Earth . HoweverThe most convincing evidence for aerosol modulation of cloud properties has been seen in the marine atmosphere , specifiThe effect of an change in atmospheric aerosol concentrations on the distribution and radiative properties of Earth’s clouds is the most uncertain component in model projections of the global radiative forcing of climate . This maThe role of natural aerosol emissions in cloud radiative forcing is less certain but thought to be even greater than that due to anthropogenic aerosol . With reSymbiodinium (Symbiodinium) is to some degree involved in the synthesis and cycling of these sulfur compounds , a central molecule in the marine sulfur cycle and precursor of DMS . Both DMiodinium  and coraiodinium  which, tiodinium . It has ompounds .Acropora corals  was noted some decades ago in the Florida keys, where extremely high concentrations of atmospheric DMS were observed after aerial exposure of the reef at low tide , and lata corals , the domc stress . Seasonac stress . If aeric stress . Corals c stress .Notwithstanding the recent progress in the field, there is limited understanding of the mechanisms of DMS production by the coral holobiont and relatively sparse data on either dissolved or atmospheric DMS concentrations in coral reef areas. Similarly, estimates of DMS fluxes to the atmosphere from reefal environments are as yet poorly constrained and not included in global DMS data bases such as that of Although the nexus between MBA emissions and changes in the properties of maritime clouds has been debated for a long time , signifiIn pristine coral reefs such as the WPWP, DMS emissions are thought to be the key driver behind an ocean thermostat which suppresses ocean warming below coral thermal tolerance thresholds (~30 °C) through a build-up of low-level clouds (LLC), as shown in An 18-year time series study  of satelAcropora spp. are the dominant coral genus throughout the GBR and Indo-Pacific and are strong producers of climatically active dimethylated sulfur compounds (2) are estimated to emit 20 Gg S year−1 as DMS  See . The Greompounds . The GBR1 as DMS  and may a) also occur when the coral reef is aerially exposed at low tide  and dry, mild winters (May–October). Corals upregulate the biosynthesis of DMSP and catabolism to DMS during physiological stress caused by exposure to elevated SST and solar irradiance, or due to low salinity associated with seasonal rainfall and riverine discharge . These plow tide , with attide  are also capable of producing high concentrations of DMSP  biosynthesis increases with thermal and irradiance stress, followed by oxidation by ROS to DMSO in temperature-sensitive species. These species are the dominant reef-building corals in the GBR and are the strongest individual producers of DMS(P) . The rat of DMSP  and may  of DMSP . HoweverAcropora spp. favor Clade D endosymbionts when exposed to thermal stress (The ability of corals to adapt to the rapidly changing climate will govern changes to DMS emissions from the GBR. Corals have a close association with a range of microbes and therefore harbor a diverse genome  that mayl stress , which cl stress . This mal stress . Rapid rl stress . Temperal stress , althougl stress  and willBiogenic emissions of DMS are a significant source of atmospheric sulfur, which in remote marine environments, are an important source of secondary sulfate aerosols. These non-sea salt sulfates play a significant role in the local climate of these remote marine environments, yet emissions of natural aerosols and their precursors remain one of the largest contributors to our uncertainty in aerosol radiative forcing , and ultPristine coral reef dense regions such as the GBR, are particularly strong sources of atmospheric DMS, similar in magnitude to highly productive high-latitude oceans. Corals upregulate the biosynthesis of DMSP and catabolism to DMS in response to physiological stress, with both processes important in maintaining coral homeostasis and promoting resilience to rising ocean temperatures. It is hypothesized that coral reef emissions of DMS increase the formation and condensational growth of marine aerosols and CCN, thereby increasing the brightness, lifetime and cover of low-level marine clouds. Local cloud cover is an important determinant of the spatial extent and severity of coral physiological stress and coral bleaching. Thus, enhanced MBA emissions and LLC cover may establish a negative feedback over coral reefs to mitigate coral physiological stress.−1 emitted from the 3,000 individual coral reefs and surrounding lagoon waters significantly influences aerosol and cloud properties in north-eastern Australia. However, there remains substantial uncertainty surrounding the importance of DMS emissions in the properties of the local atmosphere above coral reefs, and what implications ongoing coral reef degradation may have on these complex biogeochemical processes.This review has discussed evidence of significant links between coral physiological stress, DMS emissions, aerosol loading and local cloud cover over coral reefs, highlighted by a case study on the GBR, Australia. Given the vast size and relatively remote, pristine location of the GBR, it is possible that the 20 GgSyrThe current rate of ocean warming and coral reef degradation increases the urgency at which we must improve our understanding of the importance of DMS in the coral reef radiative climate. Non-linear changes in DMS emissions have been reported in response to thermal and light stress in corals . HoweverIt is possible that rising ocean temperatures will lead to an increase in DMS emissions from coral reefs, although as indicated above, there are limits on the DMS increase associated with the onset of bleaching . This poThe predicted increase in the frequency and severity of mass coral bleaching events may require the implementation of biological and/or physical interventional management strategies. The propagation of temperature-tolerant coral species may allow coral reefs to recover from recent bleaching events . PhysicaThere is enormous incentive to improve our understanding of the drivers of coral resilience, including the role of dimethylated sulfur compounds in alleviating oxidative stress and influencing the radiative balance. Future research needs to focus on the quantification and characterization of the flux of these compounds from coral reefs and its influence on aerosol and cloud formation. An improved understanding of these biogeophysical processes will provide insight into how to enhance the natural defense mechanisms of corals and inform climate engineering proposals, which may need to be implemented as a last resort to conserve coral reefs in the face of ongoing climate change.


Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease that causes scarring of the lungs. The disease is associated with the usual interstitial pneumonia pattern, which was not yet fully recapitulated by an animal model. Therefore, the disease is considered ‘human specific’. miRNA-608 is a primate specific miRNA with many potential targets, such CdC42 and Interlukin-6 (IL-6) that were previously implicated in IPF pathology.To test miR-608 expression and its targets in IPF patient samples.RNA was extracted from Formalin fixed paraffin embedded tissue sections (N = 18). miRNA-608 and Cdc42 and IL-6 levels were analyzed by qPCR. Acetylcholinesterase (AChE) is another target of miRNA-608. Its’ rs17228616 allele has a single-nucleotide polymorphism causing weakened miR-608 interaction (C2098A). Thus, DNA was extracted from whole blood samples from 56 subjects with fibrosing interstitial lung disease and this region was sequenced for assessment of rs17228616 allele polymorphism.p < 0.05). Cdc42 and IL-6 levels were lower in the IPF patient samples compared with control samples . The frequency of the rs17228616 minor A-allele was 17/56 (30.4%) with all patients being heterozygous. This result is significant vs. the published Israeli cohort of healthy individuals, which reported 17% prevalence of this allele in healthy control volunteers .miR-608 is significantly overexpressed in IPF samples in comparison with controls (miR-608 is overexpressed in IPF patients. While the exact mechanism remains to be discovered, it could potentially promote fibrotic disease. Idiopathic pulmonary fibrosis (IPF) is a chronic disease that causes scarring of the lungs. The disease is characterized by progressive worsening of dyspnoea and decline in lung function. Many patients with IPF have sub-clinical or clear co-morbid conditions including pulmonary hypertension, gastroesophageal reflux, obstructive sleep apnoea, obesity, emphysema, as well as depression and anxiety , 2.The disease is limited to the lungs, and is associated with the histopathologic and/or radiologic pattern of usual interstitial pneumonia (UIP). So far, no animal model fully recapitulated the UIP pattern  and the Micro-RNAs (miRNA) are a non-coding RNA sequences, of about 22 nucleotides long that have complementary sequences to particular regions of the mRNA, often in the 3′UTR, through which regulation occurs. Regulation generally proceeds via either mRNA degradation or inhibition, leading to gene silencing, with the degree of inhibition highly depending on the degree of complementarity between the miRNA and its corresponding mRNA target , 5.Single-nucleotide polymorphisms (SNPs) are strongly associated with susceptibility to various diseases, including IPF –8. SNPs miRNA-608  is a long (25 nucleotides) primate specific miRNA. It has many potential targets, such as Rho GTPase CdC42 and Interlukin-6 (IL-6), bearing much experimental evidence , 12. It Total RNA (including miRNA) was extracted from Formalin fixed paraffin embedded (FFPE) tissue sections taken from 18 IPF patients and 8 control samples, using the miRNeasy FFPE kit  according to the manufacturer’s instructions.mRNA was converted to cDNA using the reverse transcription kit . Reactions were performed with SYBR Green PCR master mix . Primer sequences  are listed in Table miRNA was converted into cDNA using the qScript microRNA cDNA synthesis kit  and was then amplified using Perfecta Universal PCR primer kit . qPCR was performed with Perfecta SYBR Green using specific primers for mir-has-608 . The reference genes (Snord 44 and RNV6) primers were supplied with the kit (sequence not available).Whole blood samples were collected from 56 subjects with fibrosing interstitial lung disease (ILD). DNA was purified from these samples using QaiSymphony (Qaigen). Their demographic data, as well as disease progression and final diagnosis were also recorded.DNA was amplified using PCRBIO HS Taq master mix (PCR Biosystems) with specific primers for the AchE: Forward 5′-CGCTGGAGCTCCTACATGGT-3′ and Reverse 5′-ATAGACTCGGCCCCGTGAT-3′. Products were purified using FastAP Thermosensitive Alkaline Phosphatase (Thermo-Fisher scientific). Then, sequencing was performed using BigDye™ Terminator v3.1 Cycle Sequencing Kit (Thermo-Fisher Scientific) according to manufacturer’s instructions. The target sequence was analysed by 3130 Genetic analyser (Applied Biosystems) for determining A/C allele single nucleotide polymorphism (SNP) at rs17228616.www.graphpad.com). ANOVA was performed to compare differences between multiple cohorts. Student’s t tests were employed to analyze differences between two groups. Frequencies were calculated using a Chi-square test. An effect was considered significant when the p value was < 0.05. All experiments were repeated at least three times.Statistical analysis was done using GraphPad Prism version 7.00 for Windows . Informed consent was obtained from all subjects.p < 0.05, Fig. http://mirdb.org/cgi-bin/search.cgi), 975 possible targets for miR-608 were identified. These results were then subjected to WEB-based Gene set analysis toolkit . We analysed the overrepresentation analysis (ORA) for disease (OMIM) functional database. Interestingly, IPF was the most significantly over-represented disease, with an enrichment ratio of 78 .The expression of miR-608 in was evaluated in IPF FFPE patient samples n = 18) versus histologically confirmed control tissue samples (n = 8). The IPF group consisted of 50% males, with the average age of 65.5 ± 8.5 years. The control group also consisted of 50% males, with the average age of 59.3 ± 18 years. There were no significant differences between the groups regarding comorbidities (other than ILD). Interestingly, a significant overexpression of miR-608 was found in IPF patient samples, in comparison with controls  could be higher in subjects with IPF. Thus, 56 patients were recruited and their DNA was sequenced for the A/C allele. Patient characteristics are listed in Table p = 0.01, OR = 2.1, CI 95% [1.19–3.9]).Of these subjects, the frequency of the A-allele was 17/56 30.4%) with all patients being heterozygous for the minor A-allele , yet this result did not reach significance.Of them, 64.5% were diagnosed with IPF and the rest with other types of ILD . In addition, we followed up these patients’ disease progression to determine whether they are rapidly progressing, as previously defined by the annual decline in FVC% –18. We fp = 0.02).When comparing between the A to the C allele groups' lung function tests at the time of diagnosis, a significantly reduced FVC at diagnosis for the A group was observed (Table Numerous pathways were implicated and major progress has been made towards understanding IPF etiology . HowevermiR-608 is located on human chromosome 10q24.31. Although not aforementioned in IPF , currentThe major SNP of miR-608 mentioned in cancer is the rs4919510 variant G allele. This SNP was suggested to affect the expression of mature miR-608, as well as that of the proinflammatory cytokines TNF-α, IL-6, and IL-1β. Nevertheless, there are conflicting results for the association between the presence of miR-608 rs4919510 and susceptibility to tumors –30. AlthTo date, limited targets of miR-608 have been confirmed in-vitro, and were mostly performed in tumor cell lines . Our stuOur work was inspired by a group of researchers who studied miR-608 in the context of anxiety . SeveralThe altered interaction of miR-608 with AChE and the resulting changes, which give rise to a higher ratio of suppression by miR-608 of its other targets, including CdC42 and IL-6, correlate to our results. Hanin et al. showed that young, healthy volunteers with the minor rs17228616 allele showed elevated blood pressure and reduced cortisol, predicting risk of aging-related diseases, such as IPF. Our cohort of patients with progressive fibrosing ILDs, mostly IPF, was shown to include significantly higher prevalence of minor A-allele in comparison to the healthy cohort presented by this group. Since both populations were from Israel, we can also assume similar genetic backgrounds. These results require further investigation in a large cohort to determine the polymorphism in this patient population.In conclusion, although the number of patients was limited, a significant effect was reached. We found that miR-608 is overexpressed in IPF patients, and that this population includes 30% of a specific SNP in AChE that was previously implicated as relevant to aging related diseases. These findings require further research in a large study cohort.


At the regional scale, DMS emissions increase atmospheric sulfur dioxide (SO2) and sulfate 2 and 3) in the air of Shanghai by 1.5%~2.5%. DMS emissions increase fine-mode ammonium particle concentration distribution by 4% and 5%, and fine-mode nss-Marine biogenic dimethyl sulfide (DMS) is an important natural source of sulfur in the atmosphere, which may play an important role in air quality. In this study, the WRF-CMAQ model is employed to assess the impact of DMS on the atmospheric environment at the regional scale of eastern coastal China and urban scale of Shanghai in 2017. A national scale database of DMS concentration in seawater is established based on the historical DMS measurements in the Yellow Sea, the Bohai Sea and the East China Sea in different seasons during 2009~2017. Results indicate that the sea-to-air emission flux of DMS varies greatly in different seasons, with the highest in summer, followed by spring and autumn, and the lowest in winter. The annual DMS emissions from the Yellow Sea, the Bohai Sea and the East China Sea are 0.008, 0.059, and 0.15 Tg S a Dimethylsulfide (DMS: CH3SCH3) is the most abundant volatile biogenic sulfide emanating from the ocean [The global emissions and environmental effects of biogenic sulfur have become increasingly important with the decline of anthropogenic sulfur dioxide  and wind speed –18. The e et al. . Bates ee et al. ,20 pointd Nguyen  reporteda et al.  reportedl et al.  reportedhina Sea –26. Yanghina Sea  estimate3) to directly produce SO2 and methanesulfonic acid (MSA), while ignoring the contribution of halogen to DMS oxidation [2 concentration over many sea areas increased by more than 40 parts per trillion by volume (pptv), and the annual average concentration of 3. Due to the high DMS emissions and high oxidant concentration, DMS showed the greatest impact in summer with the average concentration of 2 , and NO3) and the composition and mass of fine particles. Although the concentration of ozone (O3), NO3, and HOx decreased, the average concentration of secondary organic aerosol (SOA) increased by 10%. Perraud et al. [2 emissions are reduced to zero. In China, DMS-related studies generally focused on field observation. There have been few reports on the impact of DMS in China seawater on the regional and urban atmospheric environment using numerical modeling.Numerical modeling plays an important role in studying the impact of DMS on regional and urban atmospheric environments. Large-scale atmospheric chemistry models usually contain very simplified DMS chemistry, including the oxidation of DMS by hydroxyl radical (OH), nitrate , chlorine radical (Cl), chlorine monoxide (ClO) and iodine monoxide (IO). The detailed reactions are provided in The US EPA’s CMAQ modeling system version 5.2 was used in this study. The CMAQ v5.2 model was configured to utilize the CB05 gas phase chemistry mechanism together with halogen chemistry (cb05eh51) ,37. DMS High-resolution simulations were performed for three nested model domains on a Lambert conic conformal projection . The out2.2.2.5, PMcoarse, SO2, NH3, NOx, BC, OC, CO, CO2, and VOC). The terrestrial anthropogenic emissions inventory in the Yangtze River Delta in 2017 was provided by the Shanghai Academy of Environmental Sciences (SAES) at a 4 km × 4 km resolution and included nine pollutants . Since DMS is a marine biogenic sulfur gas, in addition to the above land-based emission inventories, anthropogenic emissions, especially anthropogenic sulfur emissions over the ocean areas are also of great importance. The shipping emissions were then constructed using o model developed by Fan et al. [2 emissions generated by human activities in January, April, July, and October were 2.3 Tg, 1.6 Tg, 1.8 Tg and 1.8 Tg, respectively, among which shipping emissions accounted for 46 Gig, 56 Gg, 63 Gg, and 62 Gg, respectively. Marine emissions of halogen species include two inorganic iodine species (HOI and I2) and nine halocarbons . The details of halogen emissions have been described elsewhere [In this study, anthropogenic emissions for the outermost domain were obtained from the Multi-Resolution Emission Inventory for China (MEIC v1.3) developed by Tsinghua University and MIX inventory developed by the Model Inter-Comparison Study for Asia (MICS-Asia III) and Hemispheric Transport of Air Pollution (HTAP) for other areas. The most recent emission inventory data is 2016 for MEIC and 2010 for MIX , which cn et al.  based onn et al. . The comlsewhere ,37.2.3.For model studies, the concentration data of DMS in seawater from the Global Surface Seawater DMS Database are generally used. The spatial and temporal distribution of DMS concentration in seawater from this global database are shown in 2.4.T [w is the concentration of DMS in seawater. Cg s the concentration of DMS in air, H is Henry’s law coefficient. The DMS concentration in seawater is about three orders of magnitude higher than that in air, so the Cg × H term can generally be ignored. The total gas transmission rate kT can be further obtained by the water side transmission rate (kw) and atmospheric gradient fraction (γa) [Ocean-atmosphere DMS fluxes are computed as the product of the concentration gradient between air and water (Δc) and the total gas transport resistance at the air/ocean interface  :(2)kT=kka can be calculated according to Kondo [10 is 10-meter wind speed, MDMS and MH2O are the molecular weights of DMS and water, respectively. It is the parameterization of kw that the largest uncertainty in DMS emission flux comes from. In this study, the Liss and Merlivat, 1986 (LM86) [kw) transfer velocity:cDMS is the Schmidt number of DMS related to the sea surface temperature SST (T/°C) [The air side transmission rate to Kondo :(4)ka=66 (LM86)  parameteT (T/°C) :(6)ScDM3.3.1.2 and PM2.5 (fine particles) concentrations from nine eastern coastal cities . The statistical metrics of normalized mean bias (NMB), root mean-square error (RMSE) and correlation coefficient (r) are calculated to quantify the degree of deviation between the observed data and simulated results, as shown in 2 and PM2.5 concentrations are underestimated by small margins with NMB in the range of −33.36% to −9.66% and −13.50% to +12.18%, respectively. Compared to those without DMS emissions, the model with DMS emissions increases SO2 concentrations and improves the comparison with observed data by small margins (<3.2%) at all cities. The addition of DMS emissions have a mixed impact on model performance for PM2.5. The model without DMS emissions already overpredicts PM2.5 at Zhoushan, Ningbo, Qinhuangdao and the addition of DMS emissions marginally deteriorates the comparison with observed data. In other cities, the model with DMS emissions improves the comparison with observed PM2.5 concentrations by small margins. The deviations between the simulation and observation occur due to the uncertainties of meteorological fields, emission inventories, chemical mechanisms, and deposition.Model performance for the regional domain is evaluated by comparing model predictions with observations of atmospheric SO2, NO2, PM2.5, and O3) are provided by the Shanghai Pudong Monitoring Station. The temporal variations of observed and predicted concentrations in January, April, July, and October in 2017 are shown in 2 is lowest (7.74%) at the Lingang Station, the NMB for NO2 is lowest (10.49%) at the Pudong Monitoring Station, the NMB for O3 is lowest (−20.60%) at the Pudong Monitoring Station, and the NMB for PM2.5 is lowest (−8.11%) at the Lingang station. The predicted O3 concentrations are underestimated with NMB ranging between −20.6% to −39.6%. However, the model can reasonably reproduce the observed spatial and temporal trends of these pollutants.For the innermost Shanghai domain, model predictions with DMS emissions are compared with observed data from three monitoring stations , Zhoupu Station , and Lingang Station ). The monitoring stations are located at distances of 5–22 km from the coast. The observed data of four major atmospheric pollutants , the Yellow Sea  and the East China Sea , the annual DMS emissions of the Bohai Sea, the Yellow Sea, and the East China Sea are 0.008, 0.059, and 0.15 Tg S a−1, respectively, which is slightly higher than the estimates of 0.074 Tg S a−1 in the Yellow Sea and East China Sea by Yang et al. [Based on the simulation results of four typical months, the annual average emission fluxes of DMS calculated by the LM86 method in the Bohai Sea, the Yellow Sea, and the East China Sea are 5.1, 6.9 and 8.8 μmol mg et al.  using thAccounting for only a small proportion of the global ocean, the Bohai Sea, the Yellow Sea, and the East China Sea are typical continental shelf areas in the world, where the emission flux of DMS is, however, far larger than that of the remote seas in all seasons. The contribution of DMS release from the continental shelf areas is not negligible, which is also consistent with the previous research results ,59.3.3.In this study, the localized database and the LM86 method are used to simulate the concentration of pollutants in the atmosphere in the four typical months. The impact of DMS emissions on the atmospheric environment in the eastern coastal region of China is examined by comparing the results with those obtained without DMS emissions.3.3.1.2, 2 concentration in four typical months. The addition of DMS emissions effectively increases SO2 levels in the atmosphere in all seasons. Higher enhancements are predicted over seawater than over coastal areas. In terms of seasons, DMS shows the highest contribution to regional atmospheric SO2concentration in summer, especially in the Bohai Sea and the East China Sea with a maximum of 0.63 μg/m3, which is consistent with the high simulated DMS emission flux there. In winter, the concentration of DMS in seawater is low, and so is the emission flux, and thus, the average contribution of DMS to the regional atmospheric SO2 concentration is only 0.12 μg/m3.The SO3. DMS has the highest contribution to the regional atmospheric 3, respectively. Unlike SO2, the enhancements in 2 in the atmosphere, while nce time , so it c2 and Overall, the release of marine biogenic DMS from China’s offshore regions enhances SO3.3.2.2 produced by the combustion of fossil fuels is also oxidized to eventually generate nss-2 emitted by human activities. It is now generally accepted that the only source of MSA in the atmosphere is the oxidation of DMS [−3, respectively, showing significant seasonal differences with higher values in summer and spring and lower ones in autumn and winter. Zhang et al. [−3 with the highest in spring and the lowest in winter, which is slightly different from our results. The observation data of the summer cruise survey in 2018 in the three China Seas show that the concentration of MSA in the atmosphere is in the range of 0.016–0.106 μg m−3, with an average of 0.045 μg m−3, which is close to our simulation results for the summer of 2017. Perraud et al. [3, which is basically consistent with our simulation results. It should be noted that the current understanding of atmospheric DMS oxidation pathways is not complete. MSA can be taken up in aerosols and clouds. Karl et al. [DMS emitted from the ocean is oxidized into MSA and non-sea salt sulfate  contributed by marine biogenic DMS. The calculated ratios of nss-bio/MSA range between 12–17, which are lower than the values of 18–20 reported by Savoie et al. [bio/nss-It can be concluded from e et al.  for the g et al.  in the Na et al.  estimatervations  and modervations  in the E3.4.Due to the confluence of marine; air masses and polluted air from coastal cities, the atmospheric environment of coastal cities will also be affected by marine biogenic DMS emissions. Therefore, under the condition of reduced anthropogenic sulfur emissions, the contribution and role of DMS emissions from polluted sea areas to the atmospheric environment of coastal cities should be reassessed.3.4.1.2 and 2, higher values appear in the Yangtze River estuary with contribution of 0.14 to 0.16 μg/m3 (about 4 to 8%). The SO2 concentration is weakly affected by DMS in the Shanghai city area near the Yangtze River estuary. Except for Baoshan and some urban district areas, where the contribution is ~0.13 μg/m3 (about 4%), the contribution of DMS to the SO2 levels in Shanghai is mostly around 0.08 μg/m3 (about 2%). The higher impact on 3 (about 10%). Compared with SO2, the impact of DMS on the 3 (about 5%). It should be noted that NOx emissions and hence NO3 radical concentrations are relatively low in clean, remote areas of the ocean. Thus, DMS is primarily oxidized by the OH initiated pathway in such areas. In contrast, NOx emissions and hence NO3 radical concentrations are higher in coastal areas with anthropogenic pollution. Thus, the NO3 pathway plays an important role in DMS oxidation in such environments. Both the day- and night-time oxidations of DMS are important in coastal areas with anthropogenic pollution while the daytime oxidation is the most important pathway in clean remote areas.The absolute and relative contribution of DMS to the annual average concentration of SO3.4.2.3 in Shanghai is shown in 3 concentration. This effect is relatively higher in tire sea area with a reduction of about 0.65~0.90 ppbv (about 1.5–2.5%). The impact of DMS emissions on the urban land area of Shanghai is relatively small, where the O3 concentration is reduced by 0.5 ppbv (about 1.25%) at the coastal line and 0.25~0.40 ppbv (about 0.5 to 1%) in most urban areas. The reactions of DMS with iodine monoxide (IO) and bromine monoxide (BrO) release iodine (I) and bromine (Br) atoms which react with O3 to reduce its concentration. In addition to the contribution to aerosols, marine biogenic DMS emissions also influence O3 which can be an area of further research.The absolute and relative contribution of DMS to the annual average concentration of O3.4.3.3− in fine-mode aerosol is quite different in the two airsheds. In the urban atmosphere of Shanghai, 16% NO3− aerosol is found in the fine-mode while only 5.2% of NO3− is present in the fine-mode in the marine atmosphere. This is likely due to the high removal rate of nitric acid in the marine air which contains relatively high concentrations of coarse alkaline sea salt particles. The fine-mode We calculate the relative amount of fine-mode aerosol in total aerosol (fine- and coarse-mode) and present the results in m et al. , our anas et al.  also sho4.−2 d−1, respectively, and the annual DMS releases can be estimated 0.008, 0.059, and 0.15 Tg S a−1, respectively. Although the continental shelf area accounts for a small proportion of the global ocean, the contribution to the global ocean DMS release cannot be ignored.A national scale database of DMS concentration in China seawater is constructed by combining the DMS measurements of historical cruise surveys from 2009 to 2017 in China’s continental shelf waters with DMS concentrations in seawater from the global database. The sea-to-air emission flux of DMS is calculated based on the database and the LM86 parameterization, which varies greatly in different seasons with the highest fluxes in summer and the lowest in winter. The DMS emissions in the entire simulated sea area for spring, summer, autumn, and winter are 0.13, 0.27,0.16, and 0.08 Tg S, respectively. The annual mean fluxes of DMS in the Bohai Sea, Yellow Sea, and East China Sea are 5.1, 6.9, and 8.8 μmol m2 in the atmosphere of the eastern coastal region of China, with the highest contribution in summer (0.63 μg/m3) and lowest in winter (0.12 μg/m3). The addition of DMS emissions also increases 3 in spring, 0.68 μg/m3 in summer, 0.32 μg/m3 in autumn and 0.20 μg/m3 in winter, respectively. Higher enhancements are predicted not only over seawater but also over coastal areas. The ratio of nss-bio/nss-At the regional scale, the addition of DMS emissions increases the concentration of SO2 levels in Shanghai is mostly around 0.08 μg/m3 (about 2%). In contrast with SO2, the impact of DMS on the 3 (about 5%). The model with DMS reduces the concentration of O3 in the atmosphere. The reduction of O3 is relatively larger in the open sea than in the urban area of Shanghai. The addition of DMS also increases fine-mode At the urban scale, the contribution of DMS to the SOSupplement1


An innovative analytical method is proposed in order to select sR values optimizing the electromagnetic wave absorption either in low-gigahertz or low-terahertz frequency range. The frequency spectra of the absorption, reflection and transmission coefficients are computed in the range up to 4 THz by using different values of sR. Then, realistic sR values of chemically doped graphene monolayers over PET substrates are considered. The designed absorbers are characterized by an absorption coefficient with a peak value of about 0.8 at the first resonant frequency of 1.1 THz, and a 1.4 THz bandwidth centered at 1.5 THz with reflection coefficient below - 10 dB. Moreover, the optical transmittance of the proposed absorbers are computed by means of the optical matrix theory and it is found to be greater than 86% in all the visible ranges.A new, transparent, metal-free absorber, based on the use of multilayer graphene/dielectric laminates (GLs), is proposed for applications in the low-terahertz frequency range. The designed absorber has a total thickness of around 70 µm and consists of a front matching dielectric layer followed by a GL, a dielectric spacer and a back GL. The laminates are periodic structures constituted of graphene sheets separated by 50-nm-thick polyethylene terephthalate (PET) interlayers, while the matching layer and the spacer are one-quarter-wavelength thick and made of PET. The GLs are modeled as homogeneous-equivalent single layers (ESLs) characterized by their sheet resistances  In the last decade, the exponential growth of wireless communications and data traffic has fueled the research in technologies operating at terahertz (THz) and low-THz frequency ranges . MoreoveIn this context, graphene, which is a two-dimensional honeycomb structure of carbon atoms, has gained a lot of interest due to its exceptional electrical, thermal, mechanical and optical properties . Current2O3 is presented in  and [Φsp] are the transfer matrices of the ith laminate iL and of the PET layers (either the spacer or the front matching layer).The overall transfer matrix [Φ] of the absorber structure sketched in Li] is given by:ig] is the short-line transfer matrix of the ith single graphene layer with conductivity iσ(ω):int] is the transfer matrix of the PET dielectric interlayer:The transfer matrix  of the PET spacer and matching layer is given by Equation (14) replacing αint with αsp = 2 πtsp/Sλ.The transfer matrix  can be computed using matrices [ΦiESL]SL instead of matrices [ΦiL] without losing accuracy.The reflection ressions :(29)R=|1dBR and the absorption coefficient A are obtained from R and T as:Finally, the reflection coefficient in decibel iN and dc conductivity iσ of the graphene layers. In order to obtain the optimum values of the sheet resistances, a new analytical formulation is developed on the basis of previously obtained results [The absorption performances of the proposed transparent screens depend strongly on the laminate-sheet resistances that are functions of the number  results .iL is modeled as the corresponding homogeneous ESLi represented by the SL transfer matrix [ ΦiESL]SL expressed in Equation (26), in which the off-diagonal coefficients are i in nanometric scale. Moreover, the equivalent effective conductivity iNiσ. Therefore, the transfer matrix [ ΦiESL]SL assumes the following simplified form.siG can be written as:Each laminate tsp = λsr/4 in which fr, being sp]r at the resonance frequency assumes the following form:r is computed as:The thickness of the spacer and matching layer sketched in inYr and the transmission coefficient Tr. These quantities are expressed as functions of the sheet conductances sG1 and sG2. It yields:The coefficients of matrix (37) are used to evaluate the corresponding input admittance inYr and Tr respectively equal to 1/η0 and T*, where T* is a target value of the transmission coefficient at frequency fr, it is possible to obtain the optimum laminate-sheet conductances. It yields:T* < 0.5. Notice that the latter equations can be used also in the low-gigahertz frequency range, as discussed in  being the overall ray transfer matrix of the considered structure  of the graphene laminate sketched in iN graphene sheets separated by iN PET interlayers is computed as:gM] and [Mint] are the ray transfer matrices of the graphene layer and of the PET interlayer. In particular, [gM] isThe ray transfer matrix [tg and (Mint] is obtained from Equations (49) and (50) replacing the graphene complex refractive index tg with the PET complex refractive index tint.WithM] of the absorber structure sketched in Msp] of the PET spacer and matching layer is obtained from matrix [Mint] replacing tint with tsp.Finally, the overall ray transfer matrix [L1 and L2 are computed by means of Equations (40) and (41) assuming εr = 3 and *T equal to 0.1, 0.07 and 0.05. The obtained values of the corresponding sheet resistances siR = 1/siG are reported in The sheet conductances of the laminates iL is modeled through the ESLi-short-line transfer matrix of Equation (26) in which σie(ω) is given by Equation (28) where iNiσ is the sheet conductance Gis of the ith laminate. The scattering time τ and the spacer thickness tsp are assumed equal to 0.1 ps and 0.035 mm, respectively. The computed frequency spectra of R, T, A and dBR are represented in *T.The reflection, transmission and absorption coefficients of the absorbers are computed in the frequency range 0.01–4 THz using Equations (29), (30), (32) and (33) combined with Equation (11). Each laminate A increases slowly as *T decreases and shows a peak value at 1.1 THz, i.e., the first resonant frequency, pretty close to 1.24 THz given by Equation (45) considering εr = 3. Furthermore, the transmission coefficient T at the resonant frequency assumes values close to the target values *T, i.e., 0.1, 0.07 and 0.05.It is worth noticing that the absorption coefficient L2 as backside reflector. If the backside reflector is made of a PEC layer, i.e., if *T = 0 and s2R = 0, it yields sR1 = 125 Ω/sq. The computed frequency spectra of coefficients R, T, A and of dBR are reported in The previous calculations are carried out considering the laminate inY of the multilayer screen is computed by using Equation (31) with τ = 0.1 ps and spt = 0.035 mm. The obtained frequency spectra of the real and imaginary parts of inY are shown in *T.For the sake of completeness, the input admittance inY are equal to (1/η0) = 2.65 ms and zero, respectively, according to the optimal absorbance conditions assumed to obtain the analytical expressions of the sheet conductances.At a first resonance frequency of 1.1 THz the real and imaginary parts of 2) laminate, sandwiched between two PET dielectric layers. A reflection coefficient of -18 dB was obtained at about 1.1 THz when the graphene-SiO2 laminate was electrically doped by a 0.02 V/nm electrostatic field bias, giving rise to a laminate-sheet resistance of about 123 Ω/sq [L2 is replaced by a PEC layer.It is interesting to compare the reflection coefficient computed when a PEC is considered as a backside reflector with the absorbance performance of the adaptive absorber described in . This ab3 chemically doped graphene layers, characterized by a dc conductivity of 8.33 ms and a sheet resistance of 120 Ω/sq [sR1 = 135 ms and sR2 = 31 ms corresponding to * =T 0.07 in L1 and four doped graphene layers in the back laminate L2.The design of a feasible absorber requires the choice of doped graphene layers characterized by suitable values of sheet resistances in order to obtain laminates having effective sheet resistances close to the ones reported in 120 Ω/sq , should R, T, A and dBR of the designed absorber are computed assuming τ = 0.1 ps and tsp = 0.035 mm, as in the previous applications, and using the overall transfer matrix Equation (11) in which each laminate is represented by the multilayer (ML) matrix of Equation (12) or by the ESL-short-line matrix of Equation (26) obtained considering the sheet resistances of the doped graphene layers and the theoretical ones evaluated in the case T* = 0.07  and (51). In particular, a sensitivity analysis of Topt with respect to the number of graphene layers N2 of the back laminate L2 and to the interlayer thickness tint is carried out. Topt spectra of the back laminate having tint = 50 nm and N2 ranging between 2 and 5, while Topt spectra of the corresponding overall structures, i.e., also considering the matching layer, the laminate L1 and the spacer. On the other hand, Topt spectra of the back laminate consisting of five doped graphene layers (N2 = 5) and interlayer thickness tint varying from 50 nm to 200 nm. Similarly, Topt of the corresponding overall structures, i.e., also considering the matching layer, the laminate L1 and the spacer.The optical transmittance Topt is always greater than 85%. Moreover, when the PET interlayer thickness is one order of magnitude lower than the considered wavelength, Topt can be easily computed by means of the approximated expression Equation (8). As the PET interlayer thickness increases, Topt cannot be computed by means of Equation (8), as shown in It can be noticed that Topt of the designed screen (with N2 = 4) is around 88% in all the visible ranges.Finally, from The proposed absorber structure consists of a front matching PET layer followed by a graphene/PET laminate, a PET spacer and a back graphene/PET laminate. The matching layer and the spacer have one-quarter-wavelength thickness. The structure is extremely simple since it does not include complex frequency-selective surfaces or metamaterials. Moreover, the designed absorbers have a total thickness of 70 µm and they are transparent and metal free, since no metal is used, also for the backside reflector.The absorption performances are evaluated through numerical calculations in the low-terahertz frequency range and both graphene conductivity and PET permittivity are considered as frequency-dependent quantities. A homogenization procedure is applied to represent the periodic graphene-PET structures as equivalent short-line single layers.3 chemically doped graphene layers are considered in order to design feasible absorbing screens with laminate-sheet resistances comparable with the theoretical ones. The computed frequency spectra of the absorption, reflection and transmission coefficients in the frequency range up to 4 THz prove the good performance of the designed absorbers and the utility of the proposed design method. In particular, the proposed screen has a 1.4 THz bandwidth centered at 1.5 THz with reflection coefficients below - 10 dB and an absorption coefficient with peak values around 0.8 at the first resonant frequency of 1.1 THz. Then, a sensitivity analysis is carried out to highlight the strong influence of the estimated scattering time on the screen performances.The optimum sheet resistances of the laminates are obtained analytically as functions of the interlayer relative permittivity and of the target transmission coefficient at the first resonance frequency. It is worth noticing that the analytical expressions of the optimum laminate-sheet resistances can be applied to both low-gigahertz or low-terahertz frequency ranges. Then, HNOFinally, the optical transmittance of the proposed absorber is computed by means of the optical matrix theory and we conclude that such transmittance is greater than 88% in all the visible ranges.


Parkinson’s disease (PD) is a common neurodegenerative disorder resulting in a range of mobility deficits affecting gait, balance and turning. In this paper, we present: (i) the development and validation of an algorithm to detect turns during gait; (ii) a method to extract turn characteristics; and (iii) the classification of PD using turn characteristics. Thirty-seven people with PD and 56 controls performed 180-degree turns during an intermittent walking task. Inertial measurement units were attached to the head, neck, lower back and ankles. A turning detection algorithm was developed and validated by two raters using video data. Spatiotemporal and signal-based characteristics were extracted and used for PD classification. There was excellent absolute agreement between the rater and the algorithm for identifying turn start and end (ICC ≥ 0.99). Classification modeling ) gave the best accuracy of 97.85% when trained on upper body and ankle data. Balanced sensitivity (97%) and specificity (96.43%) were achieved using turning characteristics from the neck, lower back and ankles. Turning characteristics, in particular angular velocity, duration, number of steps, jerk and root mean square distinguished mild-moderate PD from controls accurately and warrant future examination as a marker of mobility impairment and fall risk in PD. Parkinson’s disease (PD) affects 145,000 people across the UK . MovemenNavigating complex and unpredictable environments requires adaptations to stepping patterns and altering walking direction through turning. People with PD reportedly turn on average >60 times every hour . TurningInstrumented assessments of movement provide objective, quantifiable outcomes that offer increased sensitivity when compared to clinical scales and functional assessments ,16. EvalPrevious work has shown that turning characteristics, such as turn velocity, angle and jerk extracted from the lower back, are useful for PD classification ,28. PeopA variety of measurement outcomes may be extracted from IMUs, including those that are signal-based  and jerk) or spatiotemporal . Evaluating movement throughout the various phases of a turn  is important for enhancing our understanding of turning deficits in PD, but increases the number and dimension of the extracted features. Partial least square with discriminant analysis can overcome these issues, and may be an appropriate choice under such circumstances .Given the complexity of turning, measuring turning performance may be useful for the classification of PD. Identifying measurement outcomes that are sensitive to pathology and predict clinical endpoints is important for classification models. Therefore, the aims of this study were to: (i) optimize and validate a custom algorithm for detecting turns and phases within the turn; (ii) extract a range of turning characteristics using multiple IMUs worn on the upper and lower body; and (iii) investigate the contribution of turning characteristics and sensor location in the classification of PD. We hypothesized that characteristics associated with movement speed and smoothness and the number of steps would be important in the classification of PD. We anticipated that characteristics extracted from the upper and lower body segments would both be influential.ICICLE-GAIT is a collaborative study with ICICLE-PD, an incident cohort study  conducted between June 2009 and December 2011 . ParticiDemographics such as age, sex, height, mass, and body mass index (BMI) were obtained. Global cognition was assessed using the Mini-Mental State Examination (MMSE)  with higClinical measures were recorded for PD participants only. To assess the severity of PD motor symptoms, participants were classified according to the Hoehn and Yahr scale ranging from 0 (no symptoms) to 5 (wheelchair bound or bedridden unless aided) , as wellAs part of the gait assessment, participants completed four intermittent straight walks across an instrumented walkway. Participants initiated walking from behind a line of tape 1.5-metres from the walkway at a self-selected preferred pace until they passed another line of tape 1.5-metres beyond the end of the walkway . ParticiFive IMUs containing a tri-axial gyroscope, accelerometer and magnetometer  were attached to the participants head, neck , lower back (lumbar spine L5) and ankles , as shown in ® .For the purpose of this study, a turn was defined as a rotation of the IMU on the lower back ≥90 degrees about the vertical axis with a duration of 0.5–10 s. Gradual turns (defined as turns ≥10 degrees and ≤0.5 s in the same direction) were merged . Using t® was used to fuse the accelerometer, gyroscope and magnetometer data to estimate turn orientation. To detect possible movement in the body segments  around the vertical axis, zero-crossing of the angular velocity in the vertical direction was used. Based on the detected fluctuations in terms of yaw angles, gradual turns were combined for the head, neck and lower back IMU. The number of steps were quantified from the ankle IMUs when a turn angle of ≥30 degrees was detected around vertical axes.Turn orientation was estimated based on the integration of angular velocity about the vertical axis. Kalman Filtering  was used to estimate Euler angles to extract the orientation for all IMUs. For the upper body , static gyroscopic biases (due to factors such as temperature) and noise were removed with a compensation algorithm. This type of algorithm was chosen to avoid drift due to the integration . For theAlgorithm 1. Pseudo code for turning start and end detection.   02: for each IMU attachment #head(HD), Neck(C7), L5, Ankles   03:  store calibrated ω, a, m   04: end for   05: #Data Filtering: Noise & Drift Removal   06: Access ω   07: Compensation Algorithm: for HD, C7, L5   08: Access ω, a, m   09: Kalman Filtering (KF) Algorithm: for Ankles    10: #Every possible body segment rotation around VT   11: Access VT of ω   12: Zero-Crossing: for HD, C7, L5, Ankles   13: #Orientation Estimation   14: Integration ω VT to get Yaw: for HD, C7, L5   15: Sensor fusion with KF to Euler to Yaw: for Ankles   16: #Final Turn Detection and Estimation for HD, C7, L5   17: Yaw angle (ᴪ) extraction based on zero-crossing   18: for every ᴪ   19:   #Gradual Turns Combination   20:  if ᴪ > 10° and for next consecutive turns if intra turn   21:   duration < 0.5 s and ᴪ > 10° and same Direction   22:    Combine these gradual turns   23:  end   24:  #Turns from HD & C7   25:  if turn angle (θ) ≥ 30° and 0.5 s ≤ turn duration < 10 s   26:    Save turn start and end   27:    Save final gradual turn magnitude vector    28:  end   29:  #Turns from L5   30:  if turn angle (θ) ≥ 90° and 0.5 s ≤ turn duration < 10 s   31:    Save turn start and end   32:    Save final gradual turn magnitude vector    33:  end   34: end for   35: #Turn Transition and Estimation from Ankles    36: for every turn from L5   37:  Access the turn start and turn end from L5   38:  Access the turn direction from L5   39:  #Inner and outer turn detection to overcome direction biases   40:  if L5 direction is right   41:    Inner turn = RA   42:    Outer turn = LA   43:  else (L5 direction is left)   44:    Outer turn = RA   45:    Inner turn = LA   46:  end   47: end for   48: #Final turn transition from Ankles   49: for angles from Inner and Outer turn   50:  if inner turn angle ≥ 30°   51:    save the turn start and turn end   52:    save the turn magnitude vector   53:  end   54:  if outer turn angle ≥ 30°   55:    save the turn start and turn end   56:    save the turn magnitude vector   57:   end   58: end for #Turning features extraction based on turn start and end time   01: #Data AccessAll turns ≥90 degrees detected from the lower back IMU (L5) were considered for analysis. A 30 degree threshold was used for the head, neck, and ankles based on the lower quartile of the overall turn angle magnitude. To avoid the effect of turn direction on the ankle data, we organized ankle data according to whether they were inner  or outer  with respect to the turn. The start of a turn (turn start) and the end (turn end) were identified for each IMU, based on the algorithm in Algorithm 1.Data from 26 participants (10 PD and 16 controls) who were not included in the validation analyses were used for algorithm development and optimization , who had at least three turns  were reviewed by two independent raters. To be consistent across both groups (PD vs. CL), the first three turns were considered for validation. Raters were instructed to record the frame number of the turn start and turn end using predetermined definitions based on the following visible kinematic events. Turn start was defined as the frame at which the toe off occurs for the step which initiated a change in walking direction . Turn end was defined as the frame at which heel strike occurs for the step, which concludes the change in walking direction. Both raters were blinded to which group participants were in.The raters provided a number of additional annotations, including turn direction, and the presence and proximity of a researcher walking beside the participant. These outcomes were used for descriptive purposes only. We also asked the raters to nominate which group (PD or control) they believed the participant in the video belonged to. This information was compared to the results of the classification analysis .2) were computed. Signal-based turning characteristics were extracted from each IMU (accelerometer and gyroscope) in each axis . The number of steps to complete a turn was extracted from the ankle IMUs. Feature extraction was performed in MATLAB®.Based on the turn detection protocol described in 2 index) and the variance captured by the independent  and dependent variables  were assessed. Increasing the number of model components can increase the captured variance while reducing the overall predictive performance leading to unreliable models. The number of components was selected based on the cumulated Q2 index, which assesses the global fitness of the model. The cumulated Q2 index for each component should be > 0, with values close to 1 considered important [Algorithm 2. Pseudo code for turning features extraction.    02: Access turn start and end timings with turning angle magnitude    03: #Spatiotemporal turning characteristics    04: for total turns detected    05:  Turn time = mean of (turn end time − turn start time)    06:  Turn angle = mean of turning angle magnitude vector    07:  minimum, maximum, and variability in turn time and angle vector    08:  #Full turn angular velocity    09:  Angular velocity = Turn angle/Turn time    10:   #Angular frequency (angular velocity) in start, mid & end of turn    11:  selection of 0.1 s in the start, mid, end of within turn    12:  mean & variability in     13:  mean & variability in     14:  #Direction of turn    15:  left turn if angle magnitude is negative or right otherwise    16:  #Number of turn/transitions    17:  length of start and end time vector    18: end for    19: #Signal based turning characteristics    20: Accessing the ω, a in VT, AP & ML directions    21: Detrend the ω, a    22: Data filtering: 4th order low-pass Butterworth filter at 20 Hz cut-off    23: Getting resultant magnitude of VT,AP & ML for ω, a    24: for each start, mid, and end of turn    25:  RMS for ω, a in VT, AP, ML & R #Turn overall, start, mid & end    26:  #Jerk: rate of change of a    27:  RMS, max, min, range of each turn jerk in VT, AP, ML & R #Turn     28:   overall, start, mid & End    29:  #Angular acceleration: rate of change of ω    30:  RMS, max, min, range of each turn angular acceleration in VT, AP,     31:   ML & R #Turn overall, start, mid & end    32: end for #classification modeling and statistical analysis    01: #Detected TurnsPartial least square with the discriminant analysis (PLS-DA) method was used for the classification of PD from controls ,43. The mportant . Classifmportant .AlgoritFor algorithm validation, the limit of agreement (LOA) expressed both as absolute values and as a percentage of the mean were used to assess agreement between the two raters and between the raters and algorithm, for the PD and control groups separately. Spearman’s rho assessed relative agreement while the intra-class correlation ) assessed absolute agreement between raters and algorithm. Predefined acceptance ratings for ICC were: excellent (>0.900), good (0.750–0.899), moderate (0.500–0.749) and poor (<0.500) ,47. RMS t-test) and non-parametric (Mann-Whitney U-test) tests were performed on each turning characteristic depending on the distribution (Shapiro-Wilk test). Statistical analyses were performed in MATLAB® and SPSS®. A p-value < 0.05 was considered statistically significant.For visualization of group differences, turning characteristics were converted to standardized Z-scores. To evaluate group differences, parametric (student p > 0.05). Balance confidence  was significantly reduced in PD compared to controls (p < 0.001). PD participants were of mild-to-moderate disease stage (Hoehn and Yahr II-III), and LEDD was, on average, 536 mg/day. No freezing episodes were observed during the assessment.The groups were well matched for age (52 to 89 years), height, mass, BMI and global cognition (MMSE) . The groups were well matched for age, height and mass, and had a mean UPDRSIII score of 40.4 ± 12.6.A researcher walked alongside 16 PD participants (62%) and 3 control participants (8%) for all walking trials. For 7 PD participants (27%) and 26 control participants (90%), they were unaccompanied during the walking trials. The remaining participants were accompanied on some, but not all, of the trials . Of those participants for whom a researcher was present, the researcher either was close by or within reaching distance for 15 PD (58%) and 2 control (5%) participants for all walking trials. For a small number of participants (3 PD and 8 controls) a researcher was close by some of the time . To understand whether the presence of a researcher influenced turn direction (right or left) the raters reported whether participants turned towards or away from the researcher. Four PD (15%) and 2 control participants always turned towards the researcher compared with 1 PD (4%) who always turned away. The majority of participants (14 PD and 2 control participants) for whom a researcher was present demonstrated no consistent turn direction with respect to the researcher (towards or away). Bar charts depicting these descriptors are provided in A good level of agreement was observed between the raters when identifying the turn start and end for PD and controls .For the control group, both raters had excellent rho (0.99) and an ICC of 0.99 for turn start and turn end. RMSE and LOA were lower for turn start compared to turn end.For the PD group, both raters had excellent rho (0.99) and an ICC > 0.98. RMSE was lower for turn start compared to turn end. LOA were lower for turn end compared to turn start.A total of 425 turning characteristics from the head, neck, lower back, and ankle IMUs were calculated. One-hundred-and-nine turning characteristics  were extracted from the head, neck, and lower back . Forty-nine turning characteristics  were extracted from each ankle. Signal-based characteristics were extracted from each axis of the gyroscope and accelerometer .p values for all characteristics are presented in The following turning characteristics were found for each of the IMU locations: PD turned more frequently to the right compared to controls (CL); turn duration was longer; and angular velocity was reduced throughout the turn  in PD.Turning characteristics from the head: PD participants had a more frequent head rotation higher compared to CL. From the signal-based turning characteristics, PD had lower RMS of acceleration and angular velocity overall, as well as during the start, mid, and end, compared to CL and this was true for all directions. PD participants had a lower RMS, and range of jerk in all directions. RMS of jerk at the start of the turn was slightly higher for PD . RMS of jerk at the end of the turn was lower for PD compared to CL in all directions.Similarly, RMS and range of angular acceleration were lower across the turn overall, as well as the start, mid and end in PD, in all directions.Turning characteristics from the neck: Similar to the head, people with PD demonstrated more frequent and smaller neck (C7) rotations compared to CL. PD had lower RMS of acceleration and angular velocity compared to CL in all directions. Similarly, RMS of jerk and angular acceleration were lower for PD compared to CL in all directions.Turning characteristics from the lower back: Fewer turns were detected for PD  compared to CL. The turn angle was smaller (lower maximum and higher minimum) and less variable for PD compared to CL. Turn duration was more variable in PD compared to CL. RMS of acceleration and angular velocity were lower for PD compared to CL in all directions. RMS (minimum and maximum) and range of jerk were lower for PD in all directions. RMS of jerk was lower during the turn start and higher mid turn in all directions for PD. However, at the end of the turn, the RMS of jerk was lower in ML and VT directions and higher in the AP and R directions, for PD. PD also had lower RMS of angular acceleration in all directions.Turning characteristics from the inner ankle: PD participants took more steps to complete the turn. The turn angle was smaller in PD compared to CL. Angular velocity was more variable in PD compared to CL RMS of acceleration, angular velocity, jerk, and angular acceleration were lower for PD compared to CL.Turning characteristics from the outer ankle: PD participants took fewer steps with the outer ankle compared to CL. Almost the same turn angle and turn duration was observed in both groups; however, turn angle was less variable in PD compared to CL, with higher angular velocity observed in PD. RMS of acceleration, angular velocity, jerk and angular acceleration were lower for PD compared to CL.2X) and dependent (R2Y) variables, overall model quality (cumulative Q2 index), and information about the number of selected components in the model is provided in For each PLS-DA model, the explained variance captured by the independent  and inner ankle resulted in optimal classification accuracy of 97.85% with a sensitivity of 95% and specificity of 100%. The model that provided balance in sensitivity (97.30%) and specificity (96.43%) included turning characteristics from the neck, lower back and lower body (inner and outer ankle) and achieved a classification accuracy of 96.77%. Using only the spatiotemporal characteristics, optimal classification was achieved when the model included the upper body  and outer ankle resulting in a classification accuracy of 96.77% with 94.59% sensitivity and 98.21% specificity. Using only the signal-based characteristics, optimal classification was achieved when the PLS-DA was trained using all sensor locations (upper and lower body) with a classification accuracy of 90.32% with 84% sensitivity and 95% specificity.Models considering only sensors on the upper body  resulted in a classification accuracy of 94.62% with 89% sensitivity and 98% specificity. Considering only sensors on the lower body (inner and outer ankle) resulted in a classification accuracy of 70% with a sensitivity of 49% and specificity of 84%. Full body (upper and lower) spatiotemporal characteristics offered improved classification accuracy (94.64%) compared to signal-based characteristics (90.32%). Signal-based characteristics derived from the lower back resulted in a maximum accuracy of 84%; adding the inner ankle characteristics improved classification accuracy to 94% ; and adding the head and neck improved accuracy by a further 2%. Spatiotemporal characteristics from the upper body  performed better than signal-based characteristics; however, optimal results were achieved when combined. In contrast, signal-based characteristics from the lower body (inner and outer ankle) performed better than spatiotemporal characteristics alone.Using a combination of spatiotemporal and signal-based characteristics, and relying on a single sensor location, the neck IMU provided the best classification accuracy of 89.25% compared to the lower back (81.72%). Using only spatiotemporal characteristics, the neck and lower back resulted in a classification accuracy of 84% and 83%, respectively. Using only the signal-based characteristics, the lower back resulted in a classification accuracy of 85% compared to the neck (81%). In the majority of models, turning characteristics derived from the inner ankle improved the classification accuracy compared to the outer ankle, the inclusion of which reduced sensitivity.The contribution and importance of individual turning characteristics in the classification models were quantified using the VIP in the PLS-DA . CharactImportant spatiotemporal turning characteristics: Average angular velocity was an important spatiotemporal characteristic from the neck and lower back. From the lower back, turn duration ; average angular velocity (mid turn); variability in angular velocity (turn start); and variability in the peak angular velocity were important. From the inner ankle, the number and duration of transitions/steps were influential. From the neck, angular velocity  and turn duration  were important.Important signal-based turning characteristics: The most influential characteristics were the RMS of angular velocity from the neck  and lower back .Similarly, in the middle of the turn, RMS of angular velocity for the lower back  and the neck  were important. From the inner ankle, RMS of angular velocity ; RMS, minimum, maximum, and range of jerk in the VT direction; and range of angular acceleration (R) were important. From the lower back, RMS of angular acceleration ; minimum angular acceleration (VT); RMS of acceleration ; and RMS of jerk  were important. From the neck, angular acceleration ; RMS of jerk (AP) had greater importance. From the outer ankle, the maximum jerk (R) was important.In the present study, we have demonstrated that wearable technology can be used to detect turns whilst walking accurately in older adults and people with PD, and turning characteristics collected from a range of upper and lower body locations can also accurately distinguish between people with PD and age-matched controls. To the best of our knowledge, this is the first study to validate a turning algorithm for detecting the start and end of turns and quantify a comprehensive set of turning characteristics from a variety of sensor locations for PD classification. The combination of characteristics measured at the neck, lower back, and inner and outer ankles offered optimal sensitivity and specificity.We found excellent agreement between the raters and the algorithm with higher error observed for the turn end for both groups. This was particularly true for controls, who turned faster (higher angular velocity). Turning speed may have influenced accuracy when identifying or detecting turn events. To validate the detection of the start and end of each turn, we defined kinematic events that could be detected through observation compared to the algorithm (which used velocity thresholds). However, the optimized algorithm was able to detect the small transitional rotation in the body accurately from the angular velocity, which is difficult to observe from the naked eye. Any small change in the body segment angle can be quantified, based on the zero crossing of angular velocity. Thus, using wearable IMUs is critical for the accurate and objective assessment of turning across a variety of body segments.Previous studies ,23 have For comparison with published turning algorithms used with PD ,23, PhamThis is the first study to investigate using turning characteristics from a combination of IMU locations for the classification of PD. Overall, classification with a single sensor ranged 70–89% accuracy, with 35–86% sensitivity and 84–92% specificity. The IMU on the neck gave optimal classification, followed by the lower back, inner ankle, head and outer ankle. The performance was further improved when turning characteristics were combined from other IMU locations; with a combination of the neck, lower back, head and inner ankle resulting in an optimal accuracy of 98% . Our findings are in line with previous work ,28. UsinThe turning characteristics which were most influential in the classification modeling were related to RMS of angular velocity (radians/second), angular velocity (turn angle/turn duration), turn duration, jerk, angular acceleration, number of steps/transitions per turn, and RMS of acceleration. The results from this study are in line with previous findings  confirmiImportant turning characteristics reported in the present study are based on the model trained on characteristics derived from the neck (C7), lower back (L5) and lower body (inner and outer ankles), which resulted in optimal balance in sensitivity (97.30%) and specificity (96.43%) with an accuracy of 97%. In terms of body segment coordination during turning, older adults without PD sequentially rotate the head, followed by trunk and pelvis to turn ,51,52. ISeveral turning characteristics from the inner ankle, such as turn duration, angular velocity, jerk, angular acceleration, the number of steps required to complete the turn were important. From the outer ankle, the only characteristic of importance was the maximum jerk. A reduction in segment rotation can influence stepping strategy during turning . The numPrevious work has validated algorithms for turn detection based on video-derived annotations comparing the number of turns, turn phase and turn duration. In the present study, we validated an algorithm for detecting turns through identifying turn start and turn end. Developing robust means for detecting the start and end of a turn is important for subsequently derived turn characteristics. We selected thresholds based on the study setting; however, thresholds can be adjusted to capture a range of turn behaviors. Further work is required to determine appropriate thresholds for a variety of settings. Thresholds used in the lab may not be appropriate for use in the community where smaller turns may be required and thresholds may need to be adjusted for different sensor arrays and locations ,12,13,19In this study, we present turn data using a semi-structured protocol during which turning behaviors were covertly assessed. The turn angular displacement was set (180 degrees) and initiation of each turn was prompted by passing a line on the floor. Turn area, turn direction (right or left) and turn strategy were not controlled; therefore, a range of turning behaviors were included in the present analysis. A variety of strategies may be used when turning  ; howeverA strength of the present study is the use of a multi-sensor array to quantify turning characteristics for PD classification. We have shown that turning characteristics from the neck, lower back, inner and outer ankles resulted in optimal balance in sensitivity and specificity. Previous studies attached the sensor on the lower back , pelvis In this article, we demonstrated that turning during gait can be accurately identified with inertial measurement units, and turn characteristics from both the upper and lower body are important in the classification of PD. Turning characteristics extracted from the head, neck, lower back and inner ankle gave maximum accuracy of 97.85%, with the optimal balance in sensitivity (97.30%) and specificity (96.43%) achieved using turn characteristics from the neck, lower back, and inner and outer ankles. Characteristics related to movement speed (such as angular velocity), smoothness (such as jerk) and the number of steps taken to complete a turn were highly influential in the classification of PD. Further work is warranted to test the clinical utility of turning metrics captured in both laboratory and community-based settings.