Multiscale photoacoustic tomography using reversibly switchable bacterial phytochrome as a near-infrared photochromic probe.

Photoacoustic tomography (PAT) of genetically encoded probes allows for imaging of targeted biological processes deep in tissues with high spatial resolution; however, high background signals from blood can limit the achievable detection sensitivity. Here we describe a reversibly switchable nonfluorescent bacterial phytochrome for use in multiscale photoacoustic imaging, BphP1, with the most red-shifted absorption among genetically encoded probes. BphP1 binds a heme-derived biliverdin chromophore and is reversibly photoconvertible between red and near-infrared light-absorption states. We combined single-wavelength PAT with efficient BphP1 photoswitching, which enabled differential imaging with substantially decreased background signals, enhanced detection sensitivity, increased penetration depth and improved spatial resolution. We monitored tumor growth and metastasis with ∼ 100-μm resolution at depths approaching 10 mm using photoacoustic computed tomography, and we imaged individual cancer cells with a suboptical-diffraction resolution of ∼ 140 nm using photoacoustic microscopy. This technology is promising for biomedical studies at several scales.

Using Vibrational Infrared Biomolecular Spectroscopy to Detect Heat-Induced Changes of Molecular Structure in Relation to Nutrient Availability of Prairie Whole Oat Grains on a Molecular Basis

Background To our knowledge, there is little study on the interaction between nutrient availability and molecular structure changes induced by different processing methods in dairy cattle. The objective of this study was to investigate the effect of heat processing methods on interaction between nutrient availability and molecular structure in terms of functional groups that are related to protein and starch inherent structure of oat grains with two continued years and three replication of each year. Method The oat grains were kept as raw (control) or heated in an air-draft oven (dry roasting: DO) at 120 °C for 60 min and under microwave irradiation (MIO) for 6 min. The molecular structure features were revealed by vibrational infrared molecular spectroscopy. Results The results showed that rumen degradability of dry matter, protein and starch was significantly lower (P <0.05) for MIO compared to control and DO treatments. A higher protein α-helix to β-sheet and a lower amide I to starch area ratio were observed for MIO compared to DO and/or raw treatment. A negative correlation (−0.99, P < 0.01) was observed between α-helix or amide I to starch area ratio and dry matter. A positive correlation (0.99, P < 0.01) was found between protein β-sheet and crude protein. Conclusion The results reveal that oat grains are more sensitive to microwave irradiation than dry heating in terms of protein and starch molecular profile and nutrient availability in ruminants.

Near-infraread observation of supernovae with the Nordic Optical Telescope (NOT)

Supernova (SN) is an explosion of a star at the end of its lifetime. SNe are classified to two types, namely type I and II through the optical spectra. They have been categorised based on their explosion mechanism, to core collapse supernovae (CCSNe) and thermonuclear supernovae. The CCSNe group which includes types IIP, IIn, IIL, IIb, Ib, and Ic are produced when a massive star with initial mass more than 8 M⊙ explodes due to a collapse of its iron core. On the other hand, thermonuclear SNe originate from white dwarfs (WDs) made of carbon and oxygen, in a binary system. Infrared astronomy covers observations of astronomical objects in infrared radiation. The infrared sky is not completely dark and it is variable. Observations of SNe in the infrared give different information than optical observations. Data reduction is required to correct raw data from for example unusable pixels and sky background. In this project, the NOTCam package in the IRAF was used for the data reduction. For measuring magnitudes of SNe, the aperture photometry method with the Gaia program was used. In this Master’s thesis, near-infrared (NIR) observations of three supernovae of type IIn (namely LSQ13zm, SN 2009ip and SN2011jb), one type IIb (SN2012ey), in addition to one type Ic (SN2012ej) and type IIP (SN 2013gd) are studied with emphasis on luminosity and colour evolution. All observations were done with the Nordic Optical Telescope (NOT). Here, we used the classification by Mattila & Meikle (2001) [76], where the SNe are differentiated by the infrared light curves into two groups, namely ’ordinary’ and ’slowly declining’. The light curves and colour evolution of these supernovae were obtained in J, H and Ks bands. In this study, our data, combined with other observations, provide evidence to categorize LSQ13zm, SN 2012ej and SN 2012ey as being part of the ordinary type. We found interesting NIR behaviour of SN 2011jb, which lead it to be classified as a slowly declining type.

Desenvolvimento e aplicação de modelo de calibração multivariada para determinação de açúcares e ácidos orgânicos em bebidas comerciais utilizando espectroscopia no infravermelho

The routine analysis for quantization of organic acids and sugars are generally slow methods that involve the use and preparation of several reagents, require trained professional, the availability of special equipment and is expensive. In this context, it has been increasing investment in research whose purpose is the development of substitutive methods to reference, which are faster, cheap and simple, and infrared spectroscopy have been highlighted in this regard. The present study developed multivariate calibration models for the simultaneous and quantitative determination of ascorbic acid, citric, malic and tartaric and sugars sucrose, glucose and fructose, and soluble solids in juices and fruit nectars and classification models for ACP. We used methods of spectroscopy in the near infrared (Near Infrared, NIR) in association with the method regression of partial least squares (PLS). Were used 42 samples between juices and fruit nectars commercially available in local shops. For the construction of the models were performed with reference analysis using high-performance liquid chromatography (HPLC) and refractometry for the analysis of soluble solids. Subsequently, the acquisition of the spectra was done in triplicate, in the spectral range 12500 to 4000 cm-1. The best models were applied to the quantification of analytes in study on natural juices and juice samples produced in the Paraná Southwest Region. The juices used in the application of the models also underwent physical and chemical analysis. Validation of chromatographic methodology has shown satisfactory results, since the external calibration curve obtained R-square value (R2) above 0.98 and coefficient of variation (%CV) for intermediate precision and repeatability below 8.83%. Through the Principal Component Analysis (PCA) was possible to separate samples of juices into two major groups, grape and apple and tangerine and orange, while for nectars groups separated guava and grape, and pineapple and apple. Different validation methods, and pre-processes that were used separately and in combination, were obtained with multivariate calibration models with average forecast square error (RMSEP) and cross validation (RMSECV) errors below 1.33 and 1.53 g.100 mL-1, respectively and R2 above 0.771, except for malic acid. The physicochemical analysis enabled the characterization of drinks, including the pH working range (variation of 2.83 to 5.79) and acidity within the parameters Regulation for each flavor. Regression models have demonstrated the possibility of determining both ascorbic acids, citric, malic and tartaric with successfully, besides sucrose, glucose and fructose by means of only a spectrum, suggesting that the models are economically viable for quality control and product standardization in the fruit juice and nectars processing industry.

Radiative Behaviour of Gd2O3:Er/Yb nanoparticles under near infrared illumination

Upconverting nanoparticles have attracted much attention in science recently, specifically in view of medical and biological applications such as live imaging of cell temperatures or cancer treatment. The previously studied system of gadolinium oxide nanorods co-doped with erbium and ytterbium and decorated with different number densities of gold nanoparticles has been studied. So far, these particles have been proven as efficient nanothermometers in a temperature range from 300 up to 2000 K. In this work, a more detailed study on the morphological and radiative behaviour of these particles has been conducted. It was found that the laser power threshold for the onset of the black body radiation decreases strongly with the increase in the gold concentration. The temperature of the onset itself seems to remain approximately constant. The heating efficiency was determined to increase significantly with the gold concentration. The morphological study revealed that the temperature at the black body radiation threshold was not enough to induce any significant transformation in neither the nanorods nor the gold nanoparticles, as was expected from comparison with literature. However, significant changes in radiative properties and the morphology were detected for powders that underwent strong laser heating until the emission of brightly visible black body radiation.

NIR-spectroscopy for bioprocess monitoring & control

The Ingold port adaption of a free beam NIR spectrometer is tailored for optimal bioprocess monitoring and control. The device shows an excellent signal to noise ratio dedicated to a large free aperture and therefore a large sample volume. This can be seen particularly in the batch trajectories which show a high reproducibility. The robust and compact design withstands rough process environments as well as SIP/CIP cycles. Robust free beam NIR process analyzers are indispensable tools within the PAT/QbD framework for realtime process monitoring and control. They enable multiparametric, non-invasive measurements of analyte concentrations and process trajectories. Free beam NIR spectrometers are an ideal tool to define golden batches and process borders in the sense of QbD. Moreover, sophisticated data analysis both quantitative and MSPC yields directly to a far better process understanding. Information can be provided online in easy to interpret graphs which allow the operator to make fast and knowledge-based decisions. This finally leads to higher stability in process operation, better performance and less failed batches.

Towards near-infrared photoactivation of anticancer metal complexes.

171 p.

Near-infrared polarimetry in the galactic center. On properties of Sagittarius A* and a dusty S-cluster object

Near-infrared polarimetry observation is a powerful tool to study the central sources at the center of the Milky Way. My aim of this thesis is to analyze the polarized emission present in the central few light years of the Galactic Center region, in particular the non-thermal polarized emission of Sagittarius~A* (Sgr~A*), the electromagnetic manifestation of the super-massive black hole, and the polarized emission of an infrared-excess source in the literature referred to as DSO/G2. This source is in orbit about Sgr~A*. In this thesis I focus onto the Galactic Center observations at $\lambda=2.2~\mu m$ ($K_\mathrm{s}$-band) in polarimetry mode during several epochs from 2004 to 2012. The near-infrared polarized observations have been carried out using the adaptive optics instrument NAOS/CONICA and Wollaston prism at the Very Large Telescope of ESO (European Southern Observatory). Linear polarization at 2.2 $\mu m$, its flux statistics and time variation, can be used to constrain the physical conditions of the accretion process onto the central super-massive black hole. I present a statistical analysis of polarized $K_\mathrm{s}$-band emission from Sgr~A* and investigate the most comprehensive sample of near-infrared polarimetric light curves of this source up to now. I find several polarized flux excursions during the years and obtain an exponent of about 4 for the power-law fitted to polarized flux density distribution of fluxes above 5~mJy. Therefore, this distribution is closely linked to the single state power-law distribution of the total $K_\mathrm{s}$-band flux densities reported earlier by us. I find polarization degrees of the order of 20\%$\pm$10\% and a preferred polarization angle of $13^o\pm15^o$. Based on simulations of polarimetric measurements given the observed flux density and its uncertainty in orthogonal polarimetry channels, I find that the uncertainties of polarization parameters under a total flux density of $\sim 2\,{\mathrm{mJy}}$ are probably dominated by observational uncertainties. At higher flux densities there are intrinsic variations of polarization degree and angle within rather well constrained ranges. Since the emission is most likely due to optically thin synchrotron radiation, the obtained preferred polarization angle is very likely reflecting the intrinsic orientation of the Sgr~A* system i.e. an accretion disk or jet/wind scenario coupled to the super-massive black hole. Our polarization statistics show that Sgr~A* must be a stable system, both in terms of geometry, and the accretion process. I also investigate an infrared-excess source called G2 or Dusty S-cluster Object (DSO) moving on a highly eccentric orbit around the Galaxy's central black hole, Sgr~A*. I use for the first time the near-infrared polarimetric imaging data to determine the nature and the properties of DSO and obtain an improved $K_\mathrm{s}$-band identification of this source in median polarimetry images of different observing years. The source starts to deviate from the stellar confusion in 2008 data and it does not show a flux density variability based on our data set. Furthermore, I measure the polarization degree and angle of this source and conclude based on the simulations on polarization parameters that it is an intrinsically polarized source with a varying polarization angle as it approaches Sgr~A* position. I use the interpretation of the DSO polarimetry measurements to assess its possible properties.

Uso da espectroscopia de reflectância do infravermelho próximo (NIRS) para previsão da composição bromatológica de vagens de algaroba e palma forrageira.

Espécies forrageiras adaptadas às condições semiáridas são uma alternativa para reduzir os impactos negativos na cadeia produtiva de ruminantes da região Nordeste brasileira devido à sazonalidade na oferta de forragem, além de reduzir custo com o fornecimento de alimentos concentrados. Dentre as espécies, a vagem de algaroba (Prosopis juliflora SW D.C.) e palma forrageira (Opuntia e Nopalea) ganham destaque por tolerarem o déficit hídrico e produzirem em períodos onde a oferta de forragem está reduzida, além de apresentam bom valor nutricional e serem bem aceitas pelos animais. Porém, devido à variação na sua composição, seu uso na alimentação animal exige o conhecimento profundo da sua composição para a elaboração de dietas balanceadas. No entanto, devido ao custo e tempo para análise, os produtores não fazem uso da prática de análise da composição químico-bromatológica dos alimentos. Por isto, a espectroscopia de reflectância no infravermelho próximo (NIRS) representa uma importante alternativa aos métodos tradicionais. Objetivou-se com este estudo desenvolver e validar modelos de predição da composição bromatológica de vagem de algaroba e palma forrageira baseados em espectroscopia NIRS, escaneadas em dois modelos de equipamentos e com diferentes processamentos da amostra. Foram coletadas amostras de vagem de algaroba nos estados do Ceará, Bahia, Paraíba e Pernambuco, e amostras de palma forrageira nos estados do Ceará, Paraíba e Pernambuco, frescas (in natura) ou pré-secas e moídas. Para obtenção dos espectros utilizaram-se dois equipamentos NIR, Perten DA 7250 e FOSS 5000. Inicialmente os alimentos foram escaneados in natura em aparelho do modelo Perten, e, com o auxílio do software The Unscrambler 10.2 foi selecionado um grupo de amostras para o banco de calibração. As amostras selecionadas foram secas e moídas, e escaneadas novamente em equipamentos Perten e FOSS. Os valores dos parâmetros de referência foram obtidos por meio de metodologias tradicionalmente aplicadas em laboratório de nutrição animal para matéria seca (MS), matéria mineral (MM), matéria orgânica (MO), proteína bruta (PB), estrato etéreo (EE), fibra solúvel em detergente neutro (FDN), fibra solúvel em detergente ácido (FDA), hemicelulose (HEM) e digestibilidade in vitro da matéria seca (DIVMS). O desempenho dos modelos foi avaliado de acordo com os erros médios de calibração (RMSEC) e validação (RMSECV), coeficiente de determinação (R2 ) e da relação de desempenho de desvio dos modelos (RPD). A análise exploratória dos dados, por meio de tratamentos espectrais e análise de componentes principais (PCA), demonstraram que os bancos de dados eram similares entre si, dando segurança de desenvolver os modelos com todas as amostras selecionadas em um único modelo para cada alimento, algaroba e palma. Na avaliação dos resultados de referência, observou-se que a variação dos resultados para cada parâmetro corroboraram com os descritos na literatura. No desempenho dos modelos, aqueles desenvolvidos com pré-processamento da amostra (pré-secagem e moagem) se mostraram mais robustos do que aqueles construídos com amostras in natura. O aparelho NIRS Perten apresentou desempenho semelhante ao equipamento FOSS, apesar desse último cobrir uma faixa espectral maior e com intervalos de leituras menores. A técnica NIR, associada ao método de calibração multivariada de regressão por meio de quadrados mínimos (PLS), mostrou-se confiável para prever a composição químico-bromatológica de vagem de algaroba e da palma forrageira. Abstract: Forage species adapted to semi-arid conditions are an alternative to reduce the negative impacts in the feed supply for ruminants in the Brazilian Northeast region, due to seasonality in forage availability, as well as in the reducing of cost by providing concentrated feedstuffs. Among the species, mesquite pods (Prosopis juliflora SW DC) and spineless cactus (Opuntia and Nopalea) are highlighted for tolerating the drought and producion in periods where the forage is scarce, and have high nutritional value and also are well accepted by the animals. However, its use in animal diets requires a knowledge about its composition to prepare balanced diets. However, farmers usually do not use feed composition analysis, because their high cost and time-consuming. Thus, the Near Infrared Reflectance Spectroscopy in the (NIRS) is an important alternative to traditional methods. The objective of this study to develop and validate predictive models of the chemical composition of mesquite pods and spineless cactus-based NIRS spectroscopy, scanned in two different spectrometers and sample processing. Mesquite pods samples were collected in the states of Ceará, Bahia, Paraiba and Pernambuco, and samples of forage cactus in the states of Ceará, Paraíba and Pernambuco. In order to obtain the spectra, it was used two NIR equipment: Perten DA 7250 and FOSS 5000. sSpectra of samples were initially obtained fresh (as received) using Perten instrument, and with The Unscrambler software 10.2, a group of subsamples was selected to model development, keeping out redundant ones. The selected samples were dried and ground, and scanned again in both Perten and FOSS instruments. The values of the reference analysis were obtained by methods traditionally applied in animal nutrition laboratory to dry matter (DM), mineral matter (MM), organic matter (OM), crude protein (CP), ether extract (EE), soluble neutral detergent fiber (NDF), soluble acid detergent fiber (ADF), hemicellulose ( HEM) and in vitro digestibility of dry matter (DIVDM). The performance of the models was evaluated according to the Root Mean Square Error of Calibration (RMSEC) and cross-validation (RMSECV), coefficient of determination (R2 ) and the deviation of Ratio of performance Deviation of the models (RPD). Exploratory data analysis through spectral treatments and principal component analysis (PCA), showed that the databases were similar to each other, and may be treated asa single model for each feed - mesquite pods and cactus. Evaluating the reference results, it was observed that the variation were similar to those reported in the literature. Comparing the preprocessing of samples, the performance ofthose developed with preprocessing (dried and ground) of the sample were more robust than those built with fresh samples. The NIRS Perten device performance similar to FOSS equipment, although the latter cover a larger spectral range and with lower readings intervals. NIR technology associate do multivariate techniques is reliable to predict the bromatological composition of mesquite pods and cactus.

Seasonal changes in pasture quality and diet selection and their relationship with liveweight gain of steers grazing tropical grass and grass-legume pastures in northern Australia.

The variation in liveweight gain in grazing beef cattle as influenced by pasture type, season and year effects has important economic implications for mixed crop-livestock systems and the ability to better predict such variation would benefit beef producers by providing a guide for decision making. To identify key determinants of liveweight change of Brahman-cross steers grazing subtropical pastures, measurements of pasture quality and quantity, and diet quality in parallel with liveweight were made over two consecutive grazing seasons (48 and 46 weeks, respectively), on mixed Clitoria ternatea/grass, Stylosanthes seabrana/grass and grass swards (grass being a mixture of Bothriochloa insculpta cv. Bisset, Dichanthium sericeum and Panicum maximum var. trichoglume cv. Petrie). Steers grazing the legume-based pastures had the highest growth rate and gained between 64 and 142 kg more than those grazing the grass pastures in under 12 months. Using an exponential model, green leaf mass, green leaf %, adjusted green leaf % (adjusted for inedible woody legume stems), faecal near infrared reflectance spectroscopy predictions of diet crude protein and diet dry matter digestibility, accounted for 77, 74, 80, 63 and 60%, respectively, of the variation in daily weight gain when data were pooled across pasture types and grazing seasons. The standard error of the regressions indicated that 95% prediction intervals were large (+/- 0.42-0.64 kg/head.day) suggesting that derived regression relationships have limited practical application for accurately estimating growth rate. In this study, animal factors, especially compensatory growth effects, appeared to have a major influence on growth rate in relation to pasture and diet attributes. It was concluded that predictions of growth rate based only on pasture or diet attributes are unlikely to be accurate or reliable. Nevertheless, key pasture attributes such as green leaf mass and green leaf% provide a robust indication of what proportion of the potential growth rate of the grazing animals can be achieved.

Correlation between NIRS generated and chemically measured feed quality data in barley (Hordeum vulgare), and potential use in QTL analysis identification

A study was performed to investigate the value of near infrared reflectance spectroscopy (NIRS) as an alternate method to analytical techniques for identifying QTL associated with feed quality traits. Milled samples from an F6-derived recombinant inbred Tallon/Scarlett population were incubated in the rumen of fistulated cattle, recovered, washed and dried to determine the in-situ dry matter digestibility (DMD). Both pre- and post-digestion samples were analysed using NIRS to quantify key quality components relating to acid detergent fibre, starch and protein. This phenotypic data was used to identify trait associated QTL and compare them to previously identified QTL. Though a number of genetic correlations were identified between the phenotypic data sets, the only correlation of most interest was between DMD and starch digested (r = -0.382). The significance of this genetic correlation was that the NIRS data set identified a putative QTL on chromosomes 7H (LOD = 3.3) associated with starch digested. A QTL for DMD occurred in the same region of chromosome 7H, with flanking markers fAG/CAT63 and bPb-0758. The significant correlation and identification of this putative QTL, highlights the potential of technologies like NIRS in QTL analysis.

QTLs for water absorption and flour yield identified in the doubled haploid wheat population Lang/QT8766

Two key quality traits in milling wheat are flour yield (FY) and water absorption (WA). Ideally, breeders would prefer to use markers to select promising lines rather than time consuming rheology tests. In this study, we measured FY and WA on a wheat mapping population (Lang/QT8766) of 162 individuals grown in two replicated field experiments at three locations over 2 years. We also carried out near infrared reflectance spectroscopy (NIRS) predictions on the grain for these traits to see if NIRS phenotypic data could provide useful mapping results when compared to the reference phenotypic data. Several common QTLs were identified for FY and WA by both sets of data. The QTL on chromosome 4D was a consistently recurring QTL region for both traits. The QTL on chromosome 2A was positively linked to protein content which was supported by genetic correlation data. The results also indicated it was possible to obtain useful phenotypic data for mapping FY and WA using NIRS data. This would save time and costs as NIRS is quicker and cheaper than current rheology methods.

Implementation of a phase array diffuse optical tomographic imager

Diffuse optical tomography (DOT) using near-infrared (NIR) light is a promising tool for noninvasive imaging of deep tissue. This technique is capable of quantitative reconstructions of absorption coefficient inhomogeneities of tissue. The motivation for reconstructing the optical property variation is that it, and, in particular, the absorption coefficient variation, can be used to diagnose different metabolic and disease states of tissue. In DOT, like any other medical imaging modality, the aim is to produce a reconstruction with good spatial resolution and accuracy from noisy measurements. We study the performance of a phase array system for detection of optical inhomogeneities in tissue. The light transport through a tissue is diffusive in nature and can be modeled using diffusion equation if the optical parameters of the inhomogeneity are close to the optical properties of the background. The amplitude cancellation method that uses dual out-of-phase sources (phase array) can detect and locate small objects in turbid medium. The inverse problem is solved using model based iterative image reconstruction. Diffusion equation is solved using finite element method for providing the forward model for photon transport. The solution of the forward problem is used for computing the Jacobian and the simultaneous equation is solved using conjugate gradient search. The simulation studies have been carried out and the results show that a phase array system can resolve inhomogeneities with sizes of 5 mm when the absorption coefficient of the inhomogeneity is twice that of the background tissue. To validate this result, a prototype model for performing a dual-source system has been developed. Experiments are carried out by inserting an inhomogeneity of high optical absorption coefficient in an otherwise homogeneous phantom while keeping the scattering coefficient same. The high frequency (100 MHz) modulated dual out-of-phase laser source light is propagated through the phantom. The interference of these sources creates an amplitude null and a phase shift of 180° along a plane between the two sources with a homogeneous object. A solid resin phantom with inhomogeneities simulating the tumor is used in our experiment. The amplitude and phase changes are found to be disturbed by the presence of the inhomogeneity in the object. The experimental data (amplitude and the phase measured at the detector) are used for reconstruction. The results show that the method is able to detect multiple inhomogeneities with sizes of 4 mm. The localization error for a 5 mm inhomogeneity is found to be approximately 1 mm.