Subtle Spectral Changes Research Articles

Machine learning for (non–)epileptic tissue detection from the intraoperative electrocorticogram

ObjectiveClinical visual intraoperative electrocorticography (ioECoG) reading intends to localize epileptic tissue and improve epilepsy surgery outcome. We aimed to understand whether machine learning (ML) could complement ioECoG reading, how subgroups affected performance, and which ioECoG features were most important. MethodsWe included 91 ioECoG-guided epilepsy surgery patients with Engel 1A outcome. We allocated 71 training and 20 test set patients. We trained an extra trees classifier (ETC) with 14 spectral features to classify ioECoG channels as covering resected or non-resected tissue. We compared the ETC’s performance with clinical ioECoG reading and assessed whether patient subgroups affected performance. Explainable artificial intelligence (xAI) unveiled the most important ioECoG features learnt by the ETC. ResultsThe ETC outperformed clinical reading in five test set patients, was inferior in six, and both were inconclusive in nine. The ETC performed best in the tumor subgroup (area under ROC curve: 0.84 [95%CI 0.79–0.89]). xAI revealed predictors of resected (relative theta, alpha, and fast ripple power) and non-resected tissue (relative beta and gamma power). ConclusionsCombinations of subtle spectral ioECoG changes, imperceptible by the human eye, can aid healthy and pathological tissue discrimination. SignificanceML with spectral ioECoG features can support, rather than replace, clinical ioECoG reading, particularly in tumors.

Resolution enhanced two-dimensional correlation infrared spectra of biomolecular changes in muscle tissues of Gambusia affinis

The primary objective of this study is to investigate the effects of Zinc toxicity when fishes are exposed in a controlled environment using two-dimensional correlation spectral analysis. This analysis is superior as subtle spectral changes induced by an external perturbation are not readily noticeable in conventional one-dimensional spectra. A higher decrease in protein and carbohydrates and a lesser decrease in lipids (20%–28%) against the control sample occurs due to Zn toxicity. The lowest value of the bioconcentration factor for Zn shows carbohydrate utilization, which was resolved well in our two-dimensional spectral analysis. Synchronous maps show the predominant changes in protein (1668 cm−1), phospholipids (1771 cm−1), and fatty acids (1707 cm−1). The asynchronous two-dimensional spectra show alteration in phosphodiester stretching of glycogen followed by Amide I. In addition, we explored the importance of principal component analysis coupled two-dimensional spectra analysis which shows weak amide III protein changes. The hetero asynchronous shows well-resolved cross peaks of +(1047, 1389), resulting in improved resolution. The study helps in understanding biomolecular changes of muscle tissues of G. affinis under Zn toxicity using two-dimensional correlation infrared spectral analysis.

In Situ Surface-Enhanced Raman Spectroscopy on Organic Mixed Ionic-Electronic Conductors: Tracking Dynamic Doping in Light-Emitting Electrochemical Cells.

In the domain of organic mixed ionic-electronic conductors (OMIECs), simultaneous transport and coupling of ionic and electronic charges are crucial for the function of electrochemical devices in organic electronics. Understanding conduction mechanisms and chemical reactions in operational devices is pivotal for performance enhancement and is necessary for the informed and systematic development of more promising materials. Surface-enhanced Raman spectroscopy (SERS) is a potent tool for monitoring electrochemical evolution and dynamic doping in operational devices, offering enhanced sensitivity to subtle spectral changes. We demonstrate the utility of SERS for in situ tracking of doping in OMIECs in an organic light-emitting electrochemical cell (LEC) containing a conjugated polymer (poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene]; MEH-PPV), a molecular anion (lithium triflate), and an electrolyte network (poly(ethylene oxide); PEO). SERS enhancement is achieved via an interleaved layer of gold particles formed by spontaneous breakup of a deposited thin gold film. The results successfully highlight the ability of SERS to unveil time-resolved MEH-PPV doping and polaron formation, elucidating the effects of triflate ion transfer in the operating device and validating the electrochemical doping model in LECs.

Detection Capability Evaluation of Lunar Mineralogical Spectrometer: Results from Ground Experimental Data

The Chang’E-6 mission will first land on the far side of the moon and bring lunar samples back. As a hyperspectral imager aboard the Chang’E-6 lander, the Lunar Mineralogical Spectrometer (LMS), will achieve the goal of spectral detection and mineral composition analysis in the sampling area, and the data of LMS will also be compared with the results of the returned sample laboratory measurements. Visible and near-infrared hyperspectral remote sensing is an effective tool for lunar minerals identification and quantification. The ground validation experiment can be used to evaluate the detection ability of the LMS. According to the modal abundances of lunar minerals and glasses of APOLLO samples, binary mixed samples, ternary mixed samples, and seven-membered mixed samples were prepared. The samples were ground and stirred homogeneous to about 200 mesh (median particle size about 75 μm), to simulate the soil state of the lunar surface. Under the laboratory ambient condition, the 480–3200 nm spectral data of the samples were acquired using the Engineering Qualification Model (EQM) of Chang’E-5 LMS, the performance of which is consistent with the flight model of Chang’E-6 LMS. By fitting the mixed samples’ spectral data of the EQM using the Modified Gaussian Methods, the following conclusions can be drawn: The subtle spectral changes of mixed samples can be detected. The modal abundance of low-Ca pyroxene, high-Ca pyroxene, and plagioclase can be derived based on the spectral parameters such as absorption position, depth or width of the mixed samples, and the correlation coefficients R 2 are better than 82%, indicating that the LMS has good quantitative detection capability.

On the Role of a Conserved Tryptophan in the Chromophore Pocket of Cyanobacteriochrome

The cyanobacteriochrome Slr1393 can be photoconverted between a red (Pr) and green absorbing form (Pg). The recently determined crystal structures of both states suggest a major movement of Trp496 from a stacking interaction with ring D of the phycocyanobilin (PCB) chromophore in Pr to a position outside the chromophore pocket in Pg. Here, we investigated the role of this amino acid during photoconversion in solution using engineered protein variants in which Trp496 was substituted by natural and non-natural amino acids. These variants and the native protein were studied by various spectroscopic techniques (UV–vis absorption, fluorescence, IR, NIR and UV resonance Raman) complemented by theoretical approaches. Trp496 is shown to affect the electronic transition of PCB and to be essential for the thermal equilibrium between Pr and an intermediate state O600. However, Trp496 is not required to stabilize the tilted orientation of ring D in Pr, and does not play a role in the secondary structure changes of Slr1393 during the Pr/Pg transition. The present results confirm the re-orientation of Trp496 upon Pr → Pg conversion, but do not provide evidence of a major change in the microenvironment of this residue. Structural models indicate the penetration of water molecules into the chromophore pocket in both Pr and Pg states and thus water-Trp contacts, which can readily account for the subtle spectral changes between Pr and Pg. Thus, we conclude that reorientation of Trp496 during the Pr-to-Pg photoconversion in solution is not associated with a major change in the dielectric environment in the two states.

Detecting Xylella fastidiosa in a machine learning framework using Vcmax and leaf biochemistry quantified with airborne hyperspectral imagery

The bacterium Xylella fastidiosa (Xf) is a plant pathogen that can block the flow of water and nutrients through the xylem. Xf symptoms may be confounded with generic water stress responses. Here, we assessed changes in biochemical, biophysical and photosynthetic traits, inferred using biophysical models, in Xf-affected almond orchards under rainfed and irrigated conditions on the Island of Majorca (Balearic Islands, Spain). Recent research has demonstrated the early detection of Xf-infections by monitoring spectral changes associated with pigments, canopy structural traits, fluorescence emission and transpiration. Nevertheless, there is still a need to make further progress in monitoring physiological processes (e.g., photosynthesis rate) to be able to efficiently detect when Xf-infection causes subtle spectral changes in photosynthesis. This paper explores the ability of parsimonious machine learning (ML) algorithms to detect Xf-infected trees operationally, when considering a proxy of photosynthetic capacity, namely the maximum carboxylation rate (Vcmax), along with carbon-based constituents (CBC, including lignin), and leaf biochemical traits and tree-crown temperature (Tc) as an indicator of transpiration rates. The ML framework proposed here reduced the uncertainties associated with the extraction of reflectance spectra and temperature from individual tree crowns using high-resolution hyperspectral and thermal images. We showed that the relative importance of Vcmax and leaf biochemical constituents (e.g., CBC) in the ML model for the detection of Xf at early stages of development were intrinsically associated with the water and nutritional conditions of almond trees. Overall, the functional traits that were most consistently altered by Xf-infection were Vcmax, pigments, CBC, and Tc, and, particularly in rainfed-trees, anthocyanins, and Tc. The parsimonious ML model for Xf detection yielded accuracies exceeding 90% (kappa = 0.80). This study brings progress in the development of an operational ML framework for the detection of Xf outbreaks based on plant traits related to photosynthetic capacity, plant biochemistry and structural decay parameters.

Leveraging Nanomechanical Perturbations in Raman Spectro-Immunoassays to Design a Versatile Serum Biomarker Detection Platform.

While immunoassays are pivotal to medical diagnosis and bioanalytical chemistry, the current landscape of public health has catalyzed an important shift in the requirements of immunoassays that demand innovative solutions. For example, rapid, label-free, and low-cost screening of a given analyte is required to inform the best countermeasures to combat infectious diseases in a timely manner. Yet, the current design of immunoassays cannot accommodate such requirements as constraint by accumulative challenges, such as repeated incubation and washing, and the need of two types of antibodies in the sandwich format. To provide a potential solution, herein, a plasmonic Raman immunoassay with single-antibody, multivariate regression, and shift-of-peak strategies, coined as the PRISM assay, for serum biomarkers detection, is reported. The PRISM assay relies on Raman reporter-antibody conjugates to capture analytes on a plasmonic substrate. The ensuing nanomechanical perturbations to vibration of Raman reporters induce subtle but characteristic spectral changes that encode rich information related to the captured analytes. By fusing Raman spectroscopy and chemometric analysis, both Raman frequency shift- and multivariate regression models for sensitive detection of biomarkers are developed. The PRISM assay is expected to find a wide range of applications in clinical diagnosis, food safety surveillance, and environmental monitoring.

An assessment workflow to recover microplastics from complex biological matrices

A criteria-guided workflow was applied to assess the effectiveness of microplastic separation methods on complex marine biological matrices. Efficacy of four methods (nitric acid, HNO3, and potassium hydroxide, KOH, digestions, and sodium chloride, NaCl, and potassium iodide, KI, density flotations) was evaluated on four taxa (hard coral, sponge, sea squirt, sea cucumber) using five microplastics (polyethylene, polystyrene, polyethylene terephthalate, PET, polyvinylchloride, rayon). Matrix clarification was only unacceptably low for KOH. PET discoloured regardless of reagent. Rayon threads unravelled into monofilaments after exposure to all reagents, with discolouration also occurring with HNO3. Recovery rates were overall high, except for dense microplastics treated with NaCl and only KI yielded high rayon recovery efficiency. All polymers were accurately assigned, with subtle spectral changes observed. These results demonstrate specific limitations to separation methods applied to different biological matrices and microplastics and highlight the need to assess their suitability to provide estimates of microplastic contamination.

Supercapacitance in graphene oxide materials modified with tetrapyrrole dyes: a mechanistic study.

The global increase in mobile technology usage has created a need for better energy storage systems. With standard batteries reaching their technological limits, alternate energy storage methods are gaining momentum. In this study, we demonstrate a cheap and efficient way of building from scratch high-performance supercapacitors based on graphene oxide (GO) functionalized with tetrapyrrole derivatives: porphyrins and phthalocyanines. We present supercapacitors with capacitances about 30 times larger than those of the pristine graphene oxide-based counterparts. Experimental characterisation methods including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy (UV-VIS), electron paramagnetic resonance (EPR), and density functional theory (DFT) calculations revealed correlations between the structural, magnetic, electronic and thermodynamic properties of these materials and their performance as supercapacitors. Electrochemical studies indicate the complex and versatile nature of capacitive effects associated with thin layers of supramolecular composites of graphene oxide. The electrical double layer (EDL) capacitance, cation intercalation and faradaic processes are coupled. Moreover, differences in the electronic interactions between GO and tetrapyrrolic modifiers have a profound effect on the observed capacitance. At the same time, these interactions are sufficiently weak to induce only subtle spectral changes, as well as a small increase of the interlayer distance as determined by XRD measurements. The present work offers a viable strategy for manufacturing high-performance supercapacitive materials that are superior to the state of the art nanocarbon-based supercapacitors using benign electrolytes in terms of capacitance per mass unit and have the potential for application in future green energy storage technologies. Our study provides insight into the multifarious origins of supercapacitance beyond the well-known EDL mechanism.

Hyperspectral evidence of early-stage pine shoot beetle attack in Yunnan pine

Pine shoot beetle (PSB, Tomicus spp.) outbreaks cause widespread Yunnan pine (Pinus yunnanensis Franch) mortality in southwestern China. Early identification of PSB attacks could help forest managers mitigate the infestation before it turns into an outbreak. However, the subtle spectral changes and complex process of PSB-induced crown discoloration make the remote sensing approach difficult.This study employed a manipulated insect infestation experiment to reveal suitable monitoring indicators. Healthy Yunnan pine crowns were infested with PSB adults at different preset densities (light, moderate, and severe) using the bagging method. The crown damage parameters, physiological properties, and corresponding spectral data were systematically measured in time series. Partial least squares regression (PLSR) was used to retrieve crown chlorophylla+b (Cab) and relative water content (RWC) via band reflectance. Random forest (RF) was used to determine the optimum spectral variables capable of capturing dynamic variations in crown shoot damage ratio (SDR).Results showed that (1) after four weeks’ PSB shoot feeding attack, SDR reached saturated (26%-50%) for all damage levels, indicating a crown discoloration “switch point”. (2) A continuous decline was found in Cab and RWC at both shoot and crown levels during the whole infestation process, and the crown level decrease was smaller than the shoot level due to crown heterogeneous discoloration. (3) For PLSR retrieving crown Cab and RWC, estimation accuracy of healthy crowns could reach R2 = 0.70, RMSEcv = 14.60 mg/m2 for Cab, and R2 = 0.82, RMSEcv = 1.25% for RWC, respectively. However, PSB early attack impacted these retrieval accuracies. (4) Spectral differences were evident in the visible region (530–600 nm) and near-infrared (NIR) plateau (780–1300 nm) among the preset damage levels. Significant differences in spectral variables (AMP, CI, OSAVI, RD, and SR) were observed between healthy and moderately damaged crowns. (5) In RF modeling, the importance of spectral variables for SDR estimation varied throughout the shoot feeding process. OSAVI, CI, MSI, PSRI, and SR2 were the top indicators that were suitable for identifying SDR ranging from 5% to 50%. SDR could be estimated with an accuracy of R2 = 0.71, RMSEcv = 8.92%, after 4 weeks’ infestation. Simultaneously, with the RF model, 4 preset damage levels could be differentiated with an out-of-bag error of 14.94% and a kappa coefficient of 0.8835.In conclusion, this study mainly examined the physiological and spectral signatures of PSB early attacked crowns, and provided optimum spectral variables and models which will further help unmanned aerial vehicle (UAV) and satellite remote sensing identify PSB infestation at the early stages.

Unravelling the nature of intra-molecular hydrogen bonds in curcumin using in-situ low temperature spectroscopic studies

Curcumin, described as a wonder drug owing to various medicinal, viz. anticancer, antiviral, anti-inflammatory etc., properties, can also be seen as a model molecular system to study strong intra-molecular OH----O hydrogen bonds which govern its physico-chemical properties. The study of these hydrogen bonds is important to understand its binding characteristics. Here, we present systematic in-situ variable temperature studies of curcumin in the range 350–75 K using infrared spectroscopy to analyse the effects of external stresses on molecular structure and hydrogen bonding network. The results have been well supported by Raman spectroscopic studies. Our studies show striking difference in the nature of the two intra-molecular hydrogen bonds, generally considered equivalent, which form at the edges of the molecule. Also, the strongest intra-molecular hydrogen bond involving the enol group, present at the centre of the molecule, depicts a remarkable temperature induced strengthening upon cooling. The studies further indicate that the compound does not show any drastic structural transition in the measured temperature range. However, subtle spectral changes associated with reorientations of the hydrogen bonds are noticed across 210 K. These results will be useful to predict reaction pathways during chemical complexation of curcumin.

Can a Remote Sensing Approach with Hyperspectral Data Provide Early Detection and Mapping of Spatial Patterns of Black Bear Bark Stripping in Coast Redwoods?

The prevalence of black bear (Ursus americanus) bark stripping in commercial redwood (Sequoia sempervirens (D. Don) Endl.) timber stands has been increasing in recent years. This stripping is a threat to commercial timber production because of the deleterious effects on redwood tree fitness. This study sought to unveil a remote sensing method to detect these damaged trees early and map their spatial patterns. By developing a timely monitoring method, forest timber companies can manipulate their timber harvesting routines to adapt to the consequences of the problem. We explored the utility of high spatial resolution UAV-collected hyperspectral imagery as a means for early detection of individual trees stripped by black bears. A hyperspectral sensor was used to capture ultra-high spatial and spectral information pertaining to redwood trees with no damage, those that have been recently attacked by bears, and those with old bear damage. This spectral information was assessed using the Jeffries-Matusita (JM) distance to determine regions along the electromagnetic spectrum that are useful for discerning these three-health classes. While we were able to distinguish healthy trees from trees with old damage, we were unable to distinguish healthy trees from recently damaged trees due to the inherent characteristics of redwood tree growth and the subtle spectral changes within individual tree crowns for the time period assessed. The results, however, showed that with further assessment, a time window may be identified that informs damage before trees completely lose value.

Thermally altered subsurface material of asteroid (162173) Ryugu

Analyses of meteorites and theoretical models indicate that some carbonaceous near-Earth asteroids may have been thermally altered due to radiative heating during close approaches to the Sun1–3. However, the lack of direct measurements on the subsurface doesn’t allow us to distinguish thermal alteration due to radiative heating from parent-body processes. In April 2019, the Hayabusa2 mission successfully completed an artificial impact experiment on the carbonaceous near-Earth asteroid (162173) Ryugu4,5, which provided an opportunity to investigate exposed subsurface material and test potential effects of radiative heating. Here we report observations of Ryugu’s subsurface material by the Near-Infrared Spectrometer (NIRS3) on the Hayabusa2 spacecraft. Reflectance spectra of excavated material exhibit a hydroxyl (OH) absorption feature that is slightly stronger and peak-shifted compared with that observed for the surface, indicating that space weathering and/or radiative heating have caused subtle spectral changes in the uppermost surface. The strength and shape of the OH feature suggests that the subsurface material experienced heating above 300 °C, similar to the surface. In contrast, thermophysical modelling indicates that radiative heating cannot increase the temperature above 200 °C at the estimated excavation depth of 1 m, even at the smallest heliocentric distance possible for Ryugu. This supports the hypothesis that primary thermal alteration occurred on Ryugu’s parent body. Hayabusa2 created an artificial crater on Ryugu to analyse the subsurficial material of the asteroid. Results show that the subsurface is more hydrated than the surface. It experienced alteration processes that can be traced back to Ryugu’s parent body.

Spectroscopic detection of traumatic brain injury severity and biochemistry from the retina.

Traumatic brain injury (TBI) is a major burden on healthcare services worldwide, where scientific and clinical innovation is needed to provide better understanding of biochemical damage to improve both pre-hospital assessment and intensive care monitoring. Here, we present an unconventional concept of using Raman spectroscopy to measure the biochemical response to the retina in an ex-vivo murine model of TBI. Through comparison to spectra from the brain and retina following injury, we elicit subtle spectral changes through the use of multivariate analysis, linked to a decrease in cardiolipin and indicating metabolic disruption. The ability to classify injury severity via spectra of the retina is demonstrated for severe TBI (82.0 %), moderate TBI (75.1 %) and sham groups (69.4 %). By showing that optical spectroscopy can be used to explore the eye as the window to the brain, we lay the groundwork for further exploitation of Raman spectroscopy for indirect, non-invasive assessment of brain chemistry.

OCTAVVS: A Graphical Toolbox for High-Throughput Preprocessing and Analysis of Vibrational Spectroscopy Imaging Data.

Modern vibrational spectroscopy techniques enable the rapid collection of thousands of spectra in a single hyperspectral image, allowing researchers to study spatially heterogeneous samples at micrometer resolution. A number of algorithms have been developed to correct for effects such as atmospheric absorption, light scattering by cellular structures and varying baseline levels. After preprocessing, spectra are commonly decomposed and clustered to reveal informative patterns and subtle spectral changes. Several of these steps are slow, labor-intensive and require programming skills to make use of published algorithms and code. We here present a free and platform-independent graphical toolbox that allows rapid preprocessing of large sets of spectroscopic images, including atmospheric correction and a new algorithm for resonant Mie scattering with improved speed. The software also includes modules for decomposition into constituent spectra using the popular Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) algorithm, augmented by region-of-interest selection, as well as clustering and cluster annotation.

High-Pressure Behavior of Nickel Sulfate Monohydrate: Isothermal Compressibility, Structural Polymorphism, and Transition Pathway.

Single crystals of synthetic nickel sulfate monohydrate, α-NiSO4·H2O (space-group symmetry C2/c at ambient conditions), were subject to high-pressure behavior investigations in a diamond-anvil cell up to 10.8 GPa. By means of subtle spectral changes in Raman spectra recorded at 298 K on isothermal compression, two discontinuities were identified at 2.47(1) and 6.5(5) GPa. Both transitions turn out to be apparently second order in character, as deduced from the continuous evolution of unit-cell volumes determined from single-crystal X-ray diffraction. The first structural transition from α- to β-NiSO4·H2O is an obvious ferroelastic C2/c–P1̅ transition. It is purely displacive from a structural point of view, accompanied by symmetry changes in the hydrogen-bonding scheme. The second β- to γ-NiSO4·H2O transition, further splitting the O2 (hydrogen bridge acceptor) position and violating the P1̅ space-group symmetry, is also evident from the splitting of individual bands in the Raman spectra. It can be attributed to symmetry reduction through local violation of local centrosymmetry. Lattice elasticities were obtained by fitting second-order Birch–Murnaghan equations of state to the p-V data points yielding the following zero-pressure bulk moduli values: K0 = 63.4 ± 1.0 GPa for α-NiSO4·H2O, K0 = 61.3 ± 1.9 GPa for β-NiSO4·H2O, and K0 = 68.8 ± 2.5 GPa for γ-NiSO4·H2O.

Detection and Identification of the Vibrational Markers for the Quantification of Methionine Oxidation in Therapeutic Proteins.

Methionine oxidation is a major degradation pathway in therapeutic proteins which can impact the structure and function of proteins as well as risk to drug product quality. Detecting Met oxidation in proteins by peptide mapping followed by liquid chromatography with mass spectrometry (LC-MS) is the industry standard but is also labor intensive and susceptible to artifacts. In this work, vibrational difference spectroscopy in combination with 18O isotopic shift enabled us to demonstrate the application of Raman and FTIR techniques for the detection and quantification of Met oxidation in various therapeutic proteins, including mAbs, fusion proteins, and antibody drug conjugate. Vibrational markers of Met oxidation products, such as sulfoxide and sulfone, corresponding to S═O and C-S═O stretching frequencies were unequivocally identified based 18O isotoptic shifts. The intensity of the isolated νC-S Raman band at 702 cm-1 was successfully applied to quantify the average Met oxidation level in multiple proteins. These results are further corroborated by oxidation levels measured by tryptic peptide mapping, and thus the confirmed Met oxidation levels derived from Raman and mass spectrometry are indeed consistent with each other. Thus, we demonstrate the broader application of vibrational spectroscopy to detect the subtle spectral changes associated with various chemical or physical degradation of proteins, including Met oxidation as well as higher order structural changes.

Examining water in model membranes by near infrared spectroscopy and multivariate analysis

By exploiting the sensitivity of the NIR spectrum, particularly the first overtone of water, to the number and strength of hydrogen bonds, the hydrogen bond network and water polymerization in membranes of DMPA (1,2-dimyristoyl-sn-glycero-3-phosphate) and DMPC (1,2-dimyristoyl-sn-glycero-3-phosphocholine) was investigated as a function of the temperature and the presence of this two phospholipids having the same tail but different polar head. Principal components analysis performed on the spectra was used to disclose subtle spectral changes that mirror the alteration of the vibrational energy of the water O-H bonds, as a measure of the H-bond network. Temperature showed a dominating effect on the H-bond network. Increasing temperatures diminished the number of strongly H-bonded water molecules and increased the number of weakly H-bonded waters. This main effect of temperature was missing after the subtraction of the pure water spectra from the lipid-containing ones. An intriguing secondary effect of temperature was also revealed. Phospholipids exhibited an effect qualitatively similar to that of the temperature. DMPA, and particularly DMPC, disrupted the H-bond network in the neighboring lipid-water interface, reducing water polymerization and strengthening the water O-H bonds. The type of the polar head affects the H-bonds more than duplicate the concentration of the lipid. A connection between head group structure and the effect on the H-bonds network, and the existence of two populations of water molecules are discussed.

A Wavelet-Based Area Parameter for Indirectly Estimating Copper Concentration in Carex Leaves from Canopy Reflectance

Due to the absence of evident absorption features and low concentrations, the copper (Cu) concentration in plant leaves has rarely been estimated from hyperspectral remote sensing data. The capability of remotely-sensed estimation of foliar Cu concentrations largely depends on its close relation to foliar chlorophyll concentration. To enhance the subtle spectral changes related to chlorophyll concentration under Cu stress, this study described a wavelet-based area parameter (SWT (605−720), the sum of reconstructed detail reflectance at fourth decomposition level over 605−720 nm using discrete wavelet transform) from the canopy hyperspectral reflectance (350−2500 nm, N = 71) of Carex (C. cinerascens). The results showed that Cu concentrations had negative and strong correlation with chlorophyll concentrations (r = －0.719, p < 0.001). Based on 1000 random dataset partitioning experiments, the 1000 linear calibration models provided a mean R2Val (determination coefficient of validation) value of 0.706 and an RPD (residual prediction deviation) value of 1.75 for Cu estimation. The bootstrapping and ANOVA test results showed that SWT (605−720) significantly (p < 0.05) outperformed published chlorophyll-related and wavelet-based spectral parameters. It was concluded here that the wavelet-based area parameter (i.e., SWT (605−720)) has potential ability to indirectly estimate Cu concentrations in Carex leaves through the strong correlation between Cu and chlorophyll. The method presented in this pilot study may be used to estimate the concentrations of other heavy metals. However, further research is needed to test its transferability and robustness for estimating Cu concentrations on other plant species in different biological and environmental conditions.

Identification of Highly Pathogenic Microorganisms by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Results of an Interlaboratory Ring Trial.

In the case of a release of highly pathogenic bacteria (HPB), there is an urgent need for rapid, accurate, and reliable diagnostics. MALDI-TOF mass spectrometry is a rapid, accurate, and relatively inexpensive technique that is becoming increasingly important in microbiological diagnostics to complement classical microbiology, PCR, and genotyping of HPB. In the present study, the results of a joint exercise with 11 partner institutions from nine European countries are presented. In this exercise, 10 distinct microbial samples, among them five HPB, Bacillus anthracis, Brucella canis, Burkholderia mallei, Burkholderia pseudomallei, and Yersinia pestis, were characterized under blinded conditions. Microbial strains were inactivated by high-dose gamma irradiation before shipment. Preparatory investigations ensured that this type of inactivation induced only subtle spectral changes with negligible influence on the quality of the diagnosis. Furthermore, pilot tests on nonpathogenic strains were systematically conducted to ensure the suitability of sample preparation and to optimize and standardize the workflow for microbial identification. The analysis of the microbial mass spectra was carried out by the individual laboratories on the basis of spectral libraries available on site. All mass spectra were also tested against an in-house HPB library at the Robert Koch Institute (RKI). The averaged identification accuracy was 77% in the first case and improved to >93% when the spectral diagnoses were obtained on the basis of the RKI library. The compilation of complete and comprehensive databases with spectra from a broad strain collection is therefore considered of paramount importance for accurate microbial identification.