Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks.

Diffusion-weighted magnetic resonance imaging can be used to non-invasively probe the brain microstructure. In addition, recent advances have enabled the identification of complex fiber configurations present in most of the white matter. This has improved the investigation of structural connectivity with tractography methods. Whole-brain structural connectivity networks, or connectomes, are reconstructed by parcellating the gray matter and performing tractography to determine connectivity between these regions. These complex networks can be analyzed with graph theoretical methods, which measure their global and local properties. However, as these tools have only recently been applied to structural brain networks, there is little information about the reproducibility and intercorrelation of network properties, connectivity weights and fiber tractography reconstruction parameters in the brain. We studied the reproducibility and correlation in structural brain connectivity networks reconstructed with constrained spherical deconvolution based probabilistic streamlines tractography. Diffusion-weighted data from 19 subjects were acquired with b = 2800 s/mm2 and 75 gradient orientations. Intrasubject variability was computed with residual bootstrapping. Our findings indicate that the reproducibility of graph theoretical metrics is generally excellent with the exception of betweenness centrality. A reconstruction density of approximately one million streamlines is necessary for excellent reproducibility, but the reproducibility increases further with higher densities. The reproducibility decreases, but only slightly, when switching to a higher order in constrained spherical deconvolution. Moreover, in binary networks, using sufficiently high threshold values improves the reproducibility. We show that multiple network properties and connectivity weights are highly intercorrelated. The experiments were replicated by using a test-retest dataset of 44 healthy subjects provided by the Human Connectome Project. In conclusion, our results provide guidelines for reproducible investigation of structural brain networks.

Plenoptic Image Motion Deblurring.

We propose a method to remove motion blur in a single light field captured with a moving plenoptic camera. Since motion is unknown, we resort to a blind deconvolution formulation, where one aims to identify both the blur point spread function and the latent sharp image. Even in the absence of motion, light field images captured by a plenoptic camera are affected by a non-trivial combination of both aliasing and defocus, which depends on the 3D geometry of the scene. Therefore, motion deblurring algorithms designed for standard cameras are not directly applicable. Moreover, many state of the art blind deconvolution algorithms are based on iterative schemes, where blurry images are synthesized through the imaging model. However, current imaging models for plenoptic images are impractical due to their high dimensionality. We observe that plenoptic cameras introduce periodic patterns that can be exploited to obtain highly parallelizable numerical schemes to synthesize images. These schemes allow extremely efficient GPU implementations that enable the use of iterative methods. We can then cast blind deconvolution of a blurry light field image as a regularized energy minimization to recover a sharp high-resolution scene texture and the camera motion. Furthermore, the proposed formulation can handle non-uniform motion blur due to camera shake as demonstrated on both synthetic and real light field data.

D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data.

Diffusion Weighted (DW) MRI allows for the non-invasive study of water diffusion inside living tissues. As such, it is useful for the investigation of human brain white matter (WM) connectivity in vivo through fiber tractography (FT) algorithms. Many DW-MRI tailored restoration techniques and FT algorithms have been developed. However, it is not clear how accurately these methods reproduce the WM bundle characteristics in real-world conditions, such as in the presence of noise, partial volume effect, and a limited spatial and angular resolution. The difficulty lies in the lack of a realistic brain phantom on the one hand, and a sufficiently accurate way of modeling the acquisition-related degradation on the other. This paper proposes a software phantom that approximates a human brain to a high degree of realism and that can incorporate complex brain-like structural features. We refer to it as a Diffusion BRAIN (D-BRAIN) phantom. Also, we propose an accurate model of a (DW) MRI acquisition protocol to allow for validation of methods in realistic conditions with data imperfections. The phantom model simulates anatomical and diffusion properties for multiple brain tissue components, and can serve as a ground-truth to evaluate FT algorithms, among others. The simulation of the acquisition process allows one to include noise, partial volume effects, and limited spatial and angular resolution in the images. In this way, the effect of image artifacts on, for instance, fiber tractography can be investigated with great detail. The proposed framework enables reliable and quantitative evaluation of DW-MR image processing and FT algorithms at the level of large-scale WM structures. The effect of noise levels and other data characteristics on cortico-cortical connectivity and tractography-based grey matter parcellation can be investigated as well.

A Clearer Picture of Total Variation Blind Deconvolution.

Blind deconvolution is the problem of recovering a sharp image and a blur kernel from a noisy blurry image. Recently, there has been a significant effort on understanding the basic mechanisms to solve blind deconvolution. While this effort resulted in the deployment of effective algorithms, the theoretical findings generated contrasting views on why these approaches worked. On the one hand, one could observe experimentally that alternating energy minimization algorithms converge to the desired solution. On the other hand, it has been shown that such alternating minimization algorithms should fail to converge and one should instead use a so-called Variational Bayes approach. To clarify this conundrum, recent work showed that a good image and blur prior is instead what makes a blind deconvolution algorithm work. Unfortunately, this analysis did not apply to algorithms based on total variation regularization. In this manuscript, we provide both analysis and experiments to get a clearer picture of blind deconvolution. Our analysis reveals the very reason why an algorithm based on total variation works. We also introduce an implementation of this algorithm and show that, in spite of its extreme simplicity, it is very robust and achieves a performance comparable to the top performing algorithms.

The effect of Gibbs ringing artifacts on measures derived from diffusion MRI.

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a unique method to investigate microstructural tissue properties noninvasively and is one of the most popular methods for studying the brain white matter in vivo. To obtain reliable statistical inferences with diffusion MRI, however, there are still many challenges, such as acquiring high-quality DW-MRI data (e.g., high SNR and high resolution), careful data preprocessing (e.g., correcting for subject motion and eddy current induced geometric distortions), choosing the appropriate diffusion approach (e.g., diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), or diffusion spectrum MRI), and applying a robust analysis strategy (e.g., tractography based or voxel based analysis). Notwithstanding the numerous efforts to optimize many steps in this complex and lengthy diffusion analysis pipeline, to date, a well-known artifact in MRI--i.e., Gibbs ringing (GR)--has largely gone unnoticed or deemed insignificant as a potential confound in quantitative DW-MRI analysis. Considering the recent explosion of diffusion MRI applications in biomedical and clinical applications, a systematic and comprehensive investigation is necessary to understand the influence of GR on the estimation of diffusion measures. In this work, we demonstrate with simulations and experimental DW-MRI data that diffusion estimates are significantly affected by GR artifacts and we show that an off-the-shelf GR correction procedure based on total variation already can alleviate this issue substantially.

Informed constrained spherical deconvolution (iCSD).

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. However, the voxel sizes used in DW-MRI are relatively large, making DW-MRI prone to significant partial volume effects (PVE). These PVEs can be caused both by complex (e.g. crossing) WM fiber configurations and non-WM tissue, such as gray matter (GM) and cerebrospinal fluid. High angular resolution diffusion imaging methods have been developed to correctly characterize complex WM fiber configurations, but significant non-WM PVEs are also present in a large proportion of WM voxels. In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice, the RF is modified based on tissue fractions estimated from high-resolution anatomical data. Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.

Isotropic non-white matter partial volume effects in constrained spherical deconvolution.

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple non-parallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNRs), fiber configurations, and tissue fractions. Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35-50% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM-GM interface is especially important. We suggest acquiring data with high diffusion-weighting 2500-3000 s/mm(2), reasonable SNR (~30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD.

Refuse derived soluble bio-organics enhancing tomato plant growth and productivity.

Municipal bio-refuse (CVD), containing kitchen wastes, home gardening residues and public park trimmings, was treated with alkali to yield a soluble bio-organic fraction (SBO) and an insoluble residue. These materials were characterized using elemental analysis, potentiometric titration, and 13C NMR spectroscopy, and then applied as organic fertilizers to soil for tomato greenhouse cultivation. Their performance was compared with a commercial product obtained from animal residues. Plant growth, fruit yield and quality, and soil and leaf chemical composition were the selected performance indicators. The SBO exhibited the best performance by enhancing leaf chlorophyll content, improving plant growth and fruit ripening rate and yield. No product performance-chemical composition relationship could be assessed. Solubility could be one reason for the superior performance of SBO as a tomato growth promoter. The enhancement of leaf chlorophyll content is discussed to identify a possible link with the SBO photosensitizing properties that have been demonstrated in other work, and thus with photosynthetic performance.

Waste-derived bioorganic substances for light-induced generation of reactive oxygenated species.

Urban waste-derived bioorganic substances (UW-BOS) have shown promise as chemical auxiliaries for a number of technological applications in the chemical industry and in environmental remediation. In this study, the application of these substances in the photodegradation of organic pollutants is addressed. The experimental work is specifically focused on the photolysis mechanism promoted by AC8, a UW-BOS isolated from a 2:1 w/w mixture of food and green residues, composted for 110 days, using 4-chlorophenol (4-CP) as probe molecule. The production of (⋅)OH and the ¹O2 is monitored by EPR spectroscopy. The correlation between radical species evolution and photodegradation of 4-CP is investigated. The effect of ¹O2 and (⋅)OH scavengers on the 4-CP degradation process is also checked. The results suggest that the role of these species in the photodegradation of 4-CP depends on AC8 concentration. AC8 is thereby proven to be a photosensitizer for applications in environmental remediation. The results on AC8 further support the use of urban bio-waste as a versatile source of chemical auxiliaries of biological origin for use in diversified applications.

Acid soluble bio-organic substances isolated from urban bio-waste. Chemical composition and properties of products.

As previous work proposed commercial expectations for soluble bio-organic substances (SBO) isolated from compost of urban food, gardening and park trimming residues as chemical auxiliaries, nine urban bio-wastes (BW) treated by aerobic and anaerobic digestion for 0-360 days were used to extract SBO and investigate source variability effects on product chemical composition and properties. The bio-wastes were collected over a 13732 km(2) area populated by 2.9 millions from 565 municipalities. The SBO were characterized by their content of different C types and functional groups and by their distribution coefficient (K(PEGW)) between polyethylene glycol and water. A significant linear correlation was found between K(PEGW) and the lipophilic/hydrophilic C ratio. The investigated SBO exhibited up to sixfold change of K(PEGW) demonstrating that BW available from densely populated urban areas are an interesting exploitable source of a wide variety of potential products for the chemical market.

Behavior and properties in aqueous solution of biopolymers isolated from urban refuse.

Acid soluble biopolymeric substances (SBP) were isolated from different urban biowastes comprised of a range of materials available from metropolitan areas. These biowastes provided products with a chemical nature and solubility properties changing over a wide range and, thus, allowed to assess the effect of the variability of the chemical nature on molecular conformation and surface activity in water solution. For this scope, the SBP were characterized for chemical composition and molecular weight (MW) by microanalysis, potentiometric titration, (13)C NMR spectroscopy, and size exclusion chromatography (SEC) coupled with an online multiangle light scattering (MALS) detector. These materials were found to have 67-463 kg mol(-1) MW and 6-53 polydispersity index and to contain carboxylic acid and phenol groups bonded to aromatic and aliphatic C chains. An empirical parameter (LH) was calculated for use as an index of the lipophilic/hydrophilic C atoms ratio. The products solubility properties in solvents of different polarity, surface activity, power to enhance the water solubility of hydrophobic compounds, and particle size in water solution were also investigated by measurements of the products partition coefficient between polyethylene glycol and water (KPEGW) and of air-water surface tension (γ), water-hexane interfacial tension (IFT), disperse red orange dye solubility (DS), and dynamic light scattering (DLS) versus added SBP concentration (Cs). The results indicate that LH correlates well with KPEGW and with the products surface activity properties. Both γ and DS are shown to depend on Cs, although in opposite ways, that is, higher Cs values yield lower γ and higher DS values. Both DS-Cs and γ-Cs plots showed a significant slope change at approximately the same 1.8-2.5 g L(-1) Cs value. This suggested a change of molecular conformation taking place at the above Cs values. Hydrodynamic diameter values for SBP in solution at Cs ≤ 10 g L(-1) were found to range from 130 to 300 nm, consistent with their macromolecular nature. The DLS coupled to the γ data were consistent with molecules at the water-air interphase and in the bulk water phase having different conformations, but not significantly different molecular sizes. Molecular aggregates more likely form at 50-100 g L(-1) Cs. The results confirm that urban biowastes are a sustainable source of biobased products that may have real commercial perspectives.

A waste-derived biosurfactant for the preparation of templated silica powders.

A polymeric anionic biosurfactant isolated from urban bio-wastes was used as a template for fabricating silica powders of pore size ranging from 4 to 30 nm by the sol-gel reaction of tetraethylorthosilicate and 3-aminopropyltriethoxysilane at pH 5. The morphology of the synthesized silica powders was found to depend on the size and the conformation of the biosurfactant molecules or aggregates in solution. The use of waste-derived biosurfactants as templating agents reduces the fabrication costs and the environmental impact of mesoporous materials. At the same time, it encourages the upgrade of bio-wastes from a costly disposal matter to a source of chemicals and therefore, of revenue.

Biosurfactants from urban green waste.

From waste came forth surfactants: Humic acid like substances isolated from 0-60 day-old compost display excellent surface activity and solvent properties. These biosurfactants were used to solubilize a dye in water below and above their critical micellar concentration. The biosurfactant unimers appear to have higher dye-solubilizing power than the corresponding micelles.Humic acid like substances isolated from compost show potential as chemical auxiliaries. In the present study, three surfactant samples were obtained from green waste composted for 0-60 days to assess aging effects of the source on the properties of the products. The surface activity, dye solubility enhancement, and chemical nature of these substances were compared. No differences in performance were established among the samples. They lower water surface tension and enhance the dye solubility upon increasing their concentration. However, the ratio of soluble dye to added surfactant is higher in the premicellar than in the postmicellar concentration region. Structural investigations indicated the humic acid like substances to be amphiphiles with molecular weights in the range of 1-3 x 10(5) g mol(-1). The surfactant samples were also compared to sodium dodecylbenzenesulfonate, polyacrylic acid, and soil and water humic substances. The results encourage the application of compost as a source of low-cost biosurfactant.

Persistent organic pollutants in edible marine species from the Gulf of Naples, Southern Italy.

Edible tissues from 10 marine species, collected from the Gulf of Naples in the Southern Tyrrhenian Sea (Italy) between February and July 2003, were analysed for the presence of organochlorine pesticides hexachlorobenzene (HCB) and DDTs (p,p'-DDT, p,p'-DDE, and p,p'-DDD), and 20 polychlorinated biphenyls (PCBs). The PCB levels (calculated as the sum of all the determined congeners) were found to be the highest (from 56.8 to 47909.5 ng/g on lipid basis), followed by the DDTs (sum of p,p'-DDT and its metabolites;

Organochlorine pesticides and polychlorinated biphenyls in European roe deer Capreolus capreolus resident in a protected area in Northern Italy.

In 2001, samples of liver (n = 38) and perirenal fat (n = 25) (only in males) were taken from European roe deer (Capreolus capreolus) populating a hilly, wooded protected area in the Emilia-Romagna region of Northern Italy. Samples were analyzed for organochlorine pesticides dichlorodiphenyltrichloroethane (DDT) and its metabolites, hexachlorobenzene (HCB), hexachlorocycloexane alpha-, beta- and gamma-isomers (alpha-, beta- and gamma-HCH), alpha-endosulfan, aldrin, dieldrin and endrin, and the seven PCB 'indicator' congeners IUPAC Nos. 28, 52, 101, 118, 138, 153 and 180, in order to investigate the current state of contamination of the area and to assess the possible toxicological risks for the monitored species. p,p'-DDE was detected in 95% of the liver and perirenal fat samples (n = 63), at concentrations ranging from < dl to 629.3 ng/g lipid weight, which were much higher than those found for HCB and gamma-HCH. The concentrations of the other measured organochlorine pesticides and metabolites were below the detection limit in all the samples. PCBs were found in 97% of the liver and perirenal fat samples, at total levels ranging between < dl to 414.5 ng/g lipid weight. The hepta- and hexa-chlorinated congeners 180, 153 and 138 were predominant and comprised more than 70% of the total seven PCBs in both the liver and perirenal fat samples. In the male roe deer, significantly higher concentrations of HCB, p,p'-DDE and PCBs were found in the liver than in the perirenal fat (P < 0.001). Hepatic levels of p,p'-DDE and PCBs were significantly higher in males than in females (P < 0.05 and P < 0.001, respectively). No significant differences in organochlorine concentrations were observed between juvenile and adult males in either tissue sample (P > 0.05). Females were not statistically tested because they were all adults. The levels of organochlorine contaminants were well below those associated with adverse reproductive effects and lethality in mammals.