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A new method of forecasting mortality is introduced. The method is based on the continuous-time dynamics of the Lexis diagram, which given weak assumptions implies that the death count data are Poisson distributed. The underlying mortality rates are modelled with a hidden Markov model (HMM) which enables a fully likelihood-based inference. Likelihood inference is done by particle filter methods, which avoids approximating assumptions and also suggests natural model validation measures. The proposed model class contains as special cases many previous models with the important difference that the HMM methods make it possible to estimate the model efficiently. Another difference is that the population and latent variable variability can be explicitly modelled and estimated. Numerical examples show that the model performs well and that inefficient estimation methods can severely affect forecasts.
This chapter compares two major families of ordination methods, the unconstrained and constrained ordination. We start by describing the tasks achieved with the help of unconstrained ordination and illustrate how to interpret the resulting ordination diagrams. The methods of constrained ordination allow us to build and test statistical models describing the effects of predictors (such as environmental descriptors) on multivariate response data (such as the composition of biotic communities). We discuss linear discriminant analysis separately, which aims to use a set of numerical variables to predict the membership of observations in a priori defined classes. The methods described in this chapter are accompanied by a carefully-explained guide to the R code needed for their use, in this case employing the vegan package.
The aim of this study was to test whether a positive and a negative component could be found in broadly defined schizophrenic patients. Therefore, 70 patients either in an exacerbated or in a stabilized phase were selected according to the criteria of at least 1 of the 4 following diagnostic systems: DSM III-R, Schneider, Carpenter, Langfeldt; principal component analyses (PCA) were carried out with the 9 global ratings of the Scales for Assessment of Negative and Positive Symptoms (SANS and SAPS) and with the Positive and Negative Syndrome Scale (PANSS). The PCA of the SANS-SAPS global ratings yielded a 3-factor solution explaining 72.14% of the total variance, depicting a negative, a positive and a disorganization component. The PCA of the PANSS provided a 5-factor solution with a total explained variance of 55.98%. The first 3 factors were similar to those of the SANS-SAPS global rating analysis. The results showed that the positive and negative components described in a homogeneous schizophrenic population could be replicated in a larger and more heterogeneous group of schizophrenic patients. The question regarding the sufficiency of the positive-negative dichotomy was strengthened by the presence of a third disorganization component which explained as much of the variance as the positive component.
While brain imaging studies of juvenile patients has expanded in recent years to investigate the cerebral neurophysiologic correlates of psychiatric disorders, this research field remains scarce. The aim of the present review was to cluster the main mental disorders according to the differential brain location of the imaging findings recently reported in children and adolescents reports. A second objective was to describe the worldwide distribution and the main directions of the recent magnetic resonance imaging (MRI) and positron tomography (PET) studies in these patients.
A survey of 423 MRI and PET articles published between 2005 and 2008 was performed. A principal component analysis (PCA), then an activation likelihood estimate (ALE) meta-analysis, were applied on brain regional information retrieved from articles in order to cluster the various disorders with respect to the cerebral structures where alterations were reported. Furthermore, descriptive analysis characterized the literature production.
Two hundred and seventy-four articles involving children and adolescent patients were analyzed. Both the PCA and ALE methods clustered, three groups of diagnosed psychiatric disorders, according to the brain structural and functional locations: one group of affective disorders characterized by abnormalities of the frontal-limbic regions; a group of mental disorders with “cognition deficits” mainly related to cortex abnormalities; and one psychomotor condition associated with abnormalities in the basal ganglia. The descriptive analysis indicates a focus on attention deficit hyperactivity disorders and autism spectrum disorders, a general steady rise in the number of annual reports, and lead of US research.
This cross-sectional review of child and adolescent mental disorders based on neuroimaging findings suggests overlaps of brain locations that allow to cluster the diagnosed disorders into three sets with respectively marked affective, cognitive, and psychomotor phenomenology. Furthermore, the brain imaging research effort was unequally distributed across disorders, and did not reflect their prevalence.
The terahertz (THz) regime of the electromagnetic spectrum is rich with the emerging possibilities in imaging applications with unique characteristics to screening for weapons, explosives and bio-hazards, imaging of concealed objects, water content, and skin, and these advantages can be harnessed by using the effective THz sources and detectors. In THz imaging systems, the pulsed THz sources and detectors find unique applications and thus we have emphasized on re-visiting these kinds of systems. Several novel imaging techniques which exploit the distinctive properties of the THz systems have been presented. Moreover, the THz antenna is one of the most important components of a THz imaging system as it plays a significant role in both impedance matching and power source. Therefore, the recent developments in THz antenna design for imaging applications are reviewed and the potential challenges of such THz systems are investigated. The photoconductive antennas form the basis of many THz imaging and spectroscopy systems and finds promising applications in various scientific fields. However, for the imaging applications, there is a requirement of planar and compact THz antenna sources with on-chip fabrication and high directivity in order to achieve large depth-of-field for better image resolution. Therefore, the key modalities of improving photoconductive dipole antennas performance are identified for imaging applications. Also, the ways to improve the directivity of the photoconductive dipole antenna are discussed. The main purpose of this review is to provide an assortment of all relevant literature to bring researchers up-to-date on the current state-of-the-art and potential challenges of THz antenna technology for imaging applications.
The development of the helium ion microscope (HIM) enables the imaging of both hard, inorganic materials and soft, organic or biological materials. Advantages include outstanding topographical contrast, superior resolution down to <0.5 nm at high magnification, high depth of field, and no need for conductive coatings. The instrument relies on helium atom adsorption and ionization at a cryogenically cooled tip that is atomically sharp. Under ideal conditions this arrangement provides a beam of ions that is stable for days to weeks, with beam currents in the order of picoamperes. Over time, however, this stability is lost as gaseous contamination builds up in the source region, leading to adsorbed atoms of species other than helium, which ultimately results in beam current fluctuations. This manifests itself as horizontal stripe artifacts in HIM images. We investigate post-processing methods to remove these artifacts from HIM images, such as median filtering, Gaussian blurring, fast Fourier transforms, and principal component analysis. We arrive at a simple method for completely removing beam current fluctuation effects from HIM images while maintaining the full integrity of the information within the image.
Echinochloa species are among the most troublesome weeds in
rice cultivation, and grow in a broad habitat range in Korea. Although
various ecotypes of Echinochloa have been collected as
germplasm for future studies, it has been difficult to classify them due to
their high level of morphological similarity. This study was thus conducted
to develop and investigate the phylogenetic relationships between 77
Echinochloa accessions with the use of 23 simple
sequence repeat (SSR) markers and 24 morphological traits. Of 77
Echinochloa accessions, including 57 accessions from
Korea and 5 reference species, late watergrass was clearly clustered as a
distinctive group from barnyardgrass and other Echinochloa
species. In this analysis, we also identified core genetic and morphological
markers that can be used for the future identification and classification of
Echinochloa species. Five out of 23 SSR makers produced
distinctive bands that discriminate late watergrass from barnyardgrass and
other Echinochloa species. Four morphological traits of the
reproductive organs were the most influential contributors for classifying
Echinochloa species. Although there was no clear
consensus generated in this study between SSR markers and morphological
trait analyses, our results support the potential use of the selected SSR
markers and morphological traits in future studies of
Pb-lawsonite, PbAl2[(OH)2|Si2O7]·H2O, space group Pbnm, was synthesized as crystals up to 15 μm × 5 μm × 5 μm in size by a piston cylinder technique at a pressure of ∼4 GPa and a temperature of 873 ± 10 K. Temperature-dependent powder and single-crystal X-ray diffraction (XRD) analyses partly using synchrotron radiation as well as Raman spectroscopic investigations reveal a phase transition around 445 K resulting in the Cmcm high-temperature structure. The transformation temperature is considerably higher than that of lawsonite around 273 K, which is characterized predominantly by proton order/disorder. The transition is confirmed using principal component analysis and subsequent hierarchical cluster analysis on both the powder XRD patterns and the Raman spectra. Furthermore, a non-uniform change is observed around 355 K, which is not as pronounced as the 445 K transition and apparently comes from enhanced hydrogen bonding, which stops the atom shifts in Pb-lawsonite. These are the same bonds that mainly characterize the phase transition in lawsonite around 273 K. In contrast, the structural transition of Pblawsonite at 445 K seems to originate from the interaction of the SiO4 tetrahedra and AlO6 octahedra framework with the Pb2+ cation. The structural environment of Pb2+ can be described by a 12-fold coordination above 445 K, which changes towards irregular ten-fold coordination below this temperature. An assignment of the O–H stretching Raman bands confirms moderately strong H bonds in Pb-lawsonite, whereas both strong and weak H bonds exist in lawsonite. Therefore, a further phase transition of Pblawsonite, similar to that of lawsonite around 273 K, is not expected.
Two seahorse species, Hippocampus spinosissimus and Hippocampus trimaculatus, sampled in east and west coastal waters of Peninsular Malaysia, fed mostly on crustacean prey; small caridean shrimps and amphipods as adults (both species), and copepods and larval meroplankton as juveniles (for H. trimaculatus only). The similar short relative gut length (~0.4) of both species is consistent with a carnivorous diet. Both species are considered specialists in prey selection, focusing on slow-moving epibenthic, hyperbenthic and canopy-dwelling crustaceans that dwell on the mud-sand seabed, or are associated with seagrass or mangrove areas. In this light, seahorses with their juveniles in shallow waters are vulnerable to coastal reclamation and development.
A fast method for determination of the Co-valence state by electron energy loss spectroscopy in a transmission electron microscope is presented. We suggest the distance between the Co-L3 and Co-L2 white-lines as a reliable property for the determination of Co-valence states between 2+ and 3+. The determination of the Co-L2,3 white-line distance can be automated and is therefore well suited for the evaluation of large data sets that are collected for line scans and mappings. Data with a low signal-to-noise due to short acquisition times can be processed by applying principal component analysis. The new technique was applied to study the Co-valence state of Ba0.5Sr0.5Co0.8Fe0.2O3-d (BSCF), which is hampered by the superposition of the Ba-M4,5 white-lines on the Co-L2,3 white-lines. The Co-valence state of the cubic BSCF phase was determined to be 2.2+ (±0.2) after annealing for 100 h at 650°C, compared to an increased valence state of 2.8+ (±0.2) for the hexagonal phase. These results support models that correlate the instability of the cubic BSCF phase with an increased Co-valence state at temperatures below 840°C.
We present the results of a study to optimize the principal component analysis (PCA) algorithm for planet detection, a new algorithm complementing ADI and LOCI for increasing the contrast achievable next to a bright star. We apply PCA to our Fomalhaut VLT NACO Apodizing Phase Plate NB4.05 data.
In a previous article, we studied the influence of spectral noise on a new method for three-dimensional X-ray photoelectron spectroscopy (3D XPS) imaging, which is based on analysis of the XPS peak shape [Hajati, S., Tougaard, S., Walton, J. & Fairley, N. (2008). Surf Sci602, 3064–3070]. Here, we study in more detail the influence of noise reduction by principal component analysis (PCA) on 3D XPS images of carbon contamination of a patterned oxidized silicon sample and on 3D XPS images of Ag covered by a nanoscale patterned octadiene layer. PCA is very efficient for noise reduction, and using only the three most significant PCA factors to reconstruct the spectra restores essentially all physical information in both the intensity and shape of the XPS spectra. The corresponding signal-to-noise improvement was estimated to be equivalent to a reduction by a factor of 200 in the required data acquisition time. A small additional amount of information is obtained by using up to five PCA factors, but due to the increased noise level, this information can only be extracted if the intensity of the start and end points for each spectrum are obtained as averages over several energy points.
The new generation of aerial photographers is using different wavelengths to sense archaeological features. This is effective but can be expensive. Here the authors use data already collected for environmental management purposes, and evaluate it for archaeological prospection on pasture. They explore the visibility of features in different seasons and their sensitivity to different wavelengths, using principal components analysis to seek out the best combinations. It turns out that this grassland gave up its secrets most readily in January, when nothing much was growing, and overall the method increased the number of known sites by a good margin. This study is of the greatest importance for developing the effective survey of the world's landscape, a quarter of which is under grass.
The high beam current and subangstrom resolution of aberration-corrected scanning transmission electron microscopes has enabled electron energy loss spectroscopy (EELS) mapping with atomic resolution. These spectral maps are often dose limited and spatially oversampled, leading to low counts/channel and are thus highly sensitive to errors in background estimation. However, by taking advantage of redundancy in the dataset map, one can improve background estimation and increase chemical sensitivity. We consider two such approaches—linear combination of power laws and local background averaging—that reduce background error and improve signal extraction. Principal component analysis (PCA) can also be used to analyze spectrum images, but the poor peak-to-background ratio in EELS can lead to serious artifacts if raw EELS data are PCA filtered. We identify common artifacts and discuss alternative approaches. These algorithms are implemented within the Cornell Spectrum Imager, an open source software package for spectroscopic analysis.
We demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.
In this work, a method based on Raman spectroscopy in combination with Principal Component Analysis (PCA) and Partial Least Square-Discriminant Analysis (PLS-DA) has been developed for the rapid differentiation of heterofermentative related lactobacilli. In a first approach, Lactobacillus kefir strains were discriminated from other species of heterofermentative lactobacilli: Lb. parakefir and Lb. brevis. After this first approach, PCA allowed for a clear differentiation between Lb. parakefir and Lb.brevis. For the first level of discrimination, PCA was performed on the whole spectra and also on delimited regions, defined taking into consideration the loading values. The best regions allowing a clear differentiation between Lb. kefir and non-Lb. kefir strains were found to be: the 1700–1500 cm−1, 1500–1185 cm−1 and 1800–400 (whole spectrum) cm−1 Raman ranges. In order to develop a classification rule, PLS-DA was carried out on the mentioned regions. This method permitted the discrimination and classification of the strains under study in two groups: Lb. kefir and non-Lb. kefir. The model was further validated using lactobacilli strains from different culture collections or strains isolated from kefir grains previously identified using molecular methods. The second approach based on PCA was also performed on the whole spectra and on delimited regions, being the regions 1700–1500 cm−1, 1500–1185 cm−1 and 1185–1020 cm−1, i.e., those allowing the clearest discrimination between Lb. parakefir and Lb. brevis. The results obtained in this work, allowed a clear discrimination within heterofermentative lactobacilli strains, proteins being the biological structures most determinant for this discrimination.
Variations in fish communities of shallow lakes in the Yangtze basins were investigated from September 2007 to September 2009. Six lakes were chosen for comparative study of species composition and diversity in relation to environmental variations. Lake heterogeneity was described with environmental physico-chemical variables, using principal component analysis. Sixteen families, composed of 75 species of fish were found in the studied lakes, Cyprinidae being the dominant group. Fish species were divided by habitat preference and trophic guild: benthopelagic and herbivorous fish were the most common guilds in all lakes. Species diversity and richness were significantly higher in spring, while the evenness, expressed by equitability of Simpson’s index, was not significantly different among seasons. Species richness and diversity were significantly higher in vegetated lakes (e.g. Liangzihu Lake) than in non-vegetated lakes (e.g. Biandantang Lake), with the largest area (Liangzihu Lake) harbouring the largest species richness and the greatest diversity. The relationship between environmental variables and fish assemblage were analysed using canonical correspondence analysis (CCA). The dominant gradients describing species composition and abundance among the sampling sites were: total phosphorus, total nitrogen, chlorophyll a, transparency and water depth. Our study led to the following conclusions: 1) the water quality was better - i.e. high transparency, low total phosphorus (TP) and total nitrogen (TN) and chlorophyll a- in vegetated lakes than in unvegetated lakes; 2) vegetated lakes had higher fish diversity than unvegetated lakes; 3) fish relative abundance (CPUE: number of fish per fishing pass) was significantly related to water chemical parameters. Consequently, the details of the findings are useful and relevant for developing suitable conservation strategies to sustain the integrity of fish communities in these lakes.
In this article, we use simulated and experimental data to explore how three operator-controllable parameters—(1) signal level, (2) detector resolution, and (3) number of factors chosen for analysis—affect quantitative analyses of scanning transmission electron microscopy–energy dispersive X-ray spectroscopy spectrum images processed by principal component analysis (PCA). We find that improvements in both signal level and detector resolution improve the precision of quantitative analyses, but that signal level is the most important. We also find that if the rank of the PCA solution is not chosen properly, it may be possible to improperly fit the underlying data and degrade the accuracy of results. Additionally, precision is degraded in the case when too many factors are included in the model.