The underestimated fraction: diversity, challenges and novel insights into unicellular cyanobionts of lichens.

Lichens are remarkable and classic examples of symbiotic organisms that have fascinated scientists for centuries. Yet, it has only been for a couple of decades that significant advances have focused on the diversity of their green algal and/or cyanobacterial photobionts. Cyanolichens, which contain cyanobacteria as their photosynthetic partner, include up to 10% of all known lichens and, as such, studies on their cyanobionts are much rarer compared to their green algal counterparts. For the unicellular cyanobionts, i.e. cyanobacteria that do not form filaments, these studies are even scarcer. Nonetheless, these currently include at least 10 different genera in the cosmopolitan lichen order Lichinales. An international consortium (International Network of CyanoBionts; INCb) will tackle this lack of knowledge. In this article, we discuss the status of current unicellular cyanobiont research, compare the taxonomic resolution of photobionts from cyanolichens with those of green algal lichens (chlorolichens), and give a roadmap of research on how to recondition the underestimated fraction of symbiotic unicellular cyanobacteria in lichens.

Refining the resolution of craniofacial dysmorphology in bipolar disorder as an index of brain dysmorphogenesis.

As understanding of the genetics of bipolar disorder increases, controversy endures regarding whether the origins of this illness include early maldevelopment. Clarification would be facilitated by a 'hard' biological index of fetal developmental abnormality, among which craniofacial dysmorphology bears the closest embryological relationship to brain dysmorphogenesis. Therefore, 3D laser surface imaging was used to capture the facial surface of 21 patients with bipolar disorder and 45 control subjects; 21 patients with schizophrenia were also studied. Surface images were subjected to geometric morphometric analysis in non-affine space for more incisive resolution of subtle, localised dysmorphologies that might distinguish patients from controls. Complex and more biologically informative, non-linear changes distinguished bipolar patients from control subjects. On a background of minor dysmorphology of the upper face, maxilla, midface and periorbital regions, bipolar disorder was characterised primarily by the following dysmorphologies: (a) retrusion and shortening of the premaxilla, nose, philtrum, lips and mouth (the frontonasal prominences), with (b) some protrusion and widening of the mandible-chin. The topography of facial dysmorphology in bipolar disorder indicates disruption to early development in the frontonasal process and, on embryological grounds, cerebral dysmorphogenesis in the forebrain, most likely between the 10th and 15th week of fetal life.

The definitions of three-dimensional landmarks on the human face: an interdisciplinary view.

The analysis of shape is a key part of anatomical research and in the large majority of cases landmarks provide a standard starting point. However, while the technology of image capture has developed rapidly and in particular three-dimensional imaging is widely available, the definitions of anatomical landmarks remain rooted in their two-dimensional origins. In the important case of the human face, standard definitions often require careful orientation of the subject. This paper considers the definitions of facial landmarks from an interdisciplinary perspective, including biological and clinical motivations, issues associated with imaging and subsequent analysis, and the mathematical definition of surface shape using differential geometry. This last perspective provides a route to definitions of landmarks based on surface curvature, often making use of ridge and valley curves, which is genuinely three-dimensional and is independent of orientation. Specific definitions based on curvature are proposed. These are evaluated, along with traditional definitions, in a study that uses a hierarchical (random effects) model to estimate the error variation that is present at several different levels within the image capture process. The estimates of variation at these different levels are of interest in their own right but, in addition, evidence is provided that variation is reduced at the observer level when the new landmark definitions are used.

Evaluation of Yogurt Microstructure Using Confocal Laser Scanning Microscopy and Image Analysis.

The microstructure of protein networks in yogurts defines important physical properties of the yogurt and hereby partly its quality. Imaging this protein network using confocal scanning laser microscopy (CSLM) has shown good results, and CSLM has become a standard measuring technique for fermented dairy products. When studying such networks, hundreds of images can be obtained, and here image analysis methods are essential for using the images in statistical analysis. Previously, methods including gray level co-occurrence matrix analysis and fractal analysis have been used with success. However, a range of other image texture characterization methods exists. These methods describe an image by a frequency distribution of predefined image features (denoted textons). Our contribution is an investigation of the choice of image analysis methods by performing a comparative study of 7 major approaches to image texture description. Here, CSLM images from a yogurt fermentation study are investigated, where production factors including fat content, protein content, heat treatment, and incubation temperature are varied. The descriptors are evaluated through nearest neighbor classification, variance analysis, and cluster analysis. Our investigation suggests that the texton-based descriptors provide a fuller description of the images compared to gray-level co-occurrence matrix descriptors and fractal analysis, while still being as applicable and in some cases as easy to tune.

3-D Facial Landmark Localization With Asymmetry Patterns and Shape Regression from Incomplete Local Features.

We present a method for the automatic localization of facial landmarks that integrates nonrigid deformation with the ability to handle missing points. The algorithm generates sets of candidate locations from feature detectors and performs combinatorial search constrained by a flexible shape model. A key assumption of our approach is that for some landmarks there might not be an accurate candidate in the input set. This is tackled by detecting partial subsets of landmarks and inferring those that are missing, so that the probability of the flexible model is maximized. The ability of the model to work with incomplete information makes it possible to limit the number of candidates that need to be retained, drastically reducing the number of combinations to be tested with respect to the alternative of trying to always detect the complete set of landmarks. We demonstrate the accuracy of the proposed method in the face recognition grand challenge database, where we obtain average errors of approximately 3.5 mm when targeting 14 prominent facial landmarks. For the majority of these our method produces the most accurate results reported to date in this database. Handling of occlusions and surfaces with missing parts is demonstrated with tests on the Bosphorus database, where we achieve an overall error of 4.81 and 4.25 mm for data with and without occlusions, respectively. To investigate potential limits in the accuracy that could be reached, we also report experiments on a database of 144 facial scans acquired in the context of clinical research, with manual annotations performed by experts, where we obtain an overall error of 2.3 mm, with averages per landmark below 3.4 mm for all 14 targeted points and within 2 mm for half of them. The coordinates of automatically located landmarks are made available on-line.

Automatic segmentation of mitochondria in EM data using pairwise affinity factorization and graph-based contour searching.

In this paper, we investigate the segmentation of closed contours in subcellular data using a framework that primarily combines the pairwise affinity grouping principles with a graph partitioning contour searching approach. One salient problem that precluded the application of these methods to large scale segmentation problems is the onerous computational complexity required to generate comprehensive representations that include all pairwise relationships between all pixels in the input data. To compensate for this problem, a practical solution is to reduce the complexity of the input data by applying an over-segmentation technique prior to the application of the computationally demanding strands of the segmentation process. This approach opens the opportunity to build specific shape and intensity models that can be successfully employed to extract the salient structures in the input image which are further processed to identify the cycles in an undirected graph. The proposed framework has been applied to the segmentation of mitochondria membranes in electron microscopy data which are characterized by low contrast and low signal-to-noise ratio. The algorithm has been quantitatively evaluated using two datasets where the segmentation results have been compared with the corresponding manual annotations. The performance of the proposed algorithm has been measured using standard metrics, such as precision and recall, and the experimental results indicate a high level of segmentation accuracy.

Unsupervised mitochondria segmentation using recursive spectral clustering and adaptive similarity models.

The unsupervised segmentation method proposed in the current study follows the evolutional ability of human vision to extrapolate significant structures in an image. In this work we adopt the perceptual grouping strategy by selecting the spectral clustering framework, which is known to capture perceptual organization features, as well as by developing similarity models according to Gestaltic laws of visual segregation. Our proposed framework applies but is not limited to the detection of cells and organelles in microscopic images and attempts to provide an effective alternative to presently dominating manual segmentation and tissue classification practice. The main theoretical contribution of our work resides in the formulation of robust similarity models which automatically adapt to the statistical structure of the biological domain and return optimal performance in pixel classification tasks under the wide variety of distributional assumptions.

Management of a patient with locally advanced prostate cancer with degarelix: a case report.

Gonadotropin releasing hormone (GnRH) antagonists, such as degarelix, are emerging as an androgen deprivation therapy primary agents in a treatment of advanced prostate cancer. The role of GnRH antagonists in management of lower urinary tract symptoms associated with prostate cancer has not been clearly established. In this report, we describe the case of a patient with locally advanced prostate cancer who presented with symptoms of urinary retention and renal failure. The use of degarelix in this patient led to a rapid reduction in the prostate-specific antigen level; however, obstructive symptoms persisted despite the use of degarelix and radiation treatment.

Texture enhanced histogram equalization using TV- L¹ image decomposition.

Histogram transformation defines a class of image processing operations that are widely applied in the implementation of data normalization algorithms. In this paper, we present a new variational approach for image enhancement that is constructed to alleviate the intensity saturation effects that are introduced by standard contrast enhancement (CE) methods based on histogram equalization. In this paper, we initially apply total variation (TV) minimization with a L(1) fidelity term to decompose the input image with respect to cartoon and texture components. Contrary to previous papers that rely solely on the information encompassed in the distribution of the intensity information, in this paper, the texture information is also employed to emphasize the contribution of the local textural features in the CE process. This is achieved by implementing a nonlinear histogram warping CE strategy that is able to maximize the information content in the transformed image. Our experimental study addresses the CE of a wide variety of image data and comparative evaluations are provided to illustrate that our method produces better results than conventional CE strategies.

Fully automated segmentation and tracking of the intima media thickness in ultrasound video sequences of the common carotid artery.

The robust identification and measurement of the intima media thickness (IMT) has a high clinical relevance because it represents one of the most precise predictors used in the assessment of potential future cardiovascular events. To facilitate the analysis of arterial wall thickening in serial clinical investigations, in this paper we have developed a novel fully automatic algorithm for the segmentation, measurement, and tracking of the intima media complex (IMC) in B-mode ultrasound video sequences. The proposed algorithm entails a two-stage image analysis process that initially addresses the segmentation of the IMC in the first frame of the ultrasound video sequence using a model-based approach; in the second step, a novel customized tracking procedure is applied to robustly detect the IMC in the subsequent frames. For the video tracking procedure, we introduce a spatially coherent algorithm called adaptive normalized correlation that prevents the tracking process from converging to wrong arterial interfaces. This represents the main contribution of this paper and was developed to deal with inconsistencies in the appearance of the IMC over the cardiac cycle. The quantitative evaluation has been carried out on 40 ultrasound video sequences of the common carotid artery (CCA) by comparing the results returned by the developed algorithm with respect to ground truth data that has been manually annotated by clinical experts. The measured IMT(mean) ± standard deviation recorded by the proposed algorithm is 0.60 mm ± 0.10, with a mean coefficient of variation (CV) of 2.05%, whereas the corresponding result obtained for the manually annotated ground truth data is 0.60 mm ± 0.11 with a mean CV equal to 5.60%. The numerical results reported in this paper indicate that the proposed algorithm is able to correctly segment and track the IMC in ultrasound CCA video sequences, and we were encouraged by the stability of our technique when applied to data captured under different imaging conditions. Future clinical studies will focus on the evaluation of patients that are affected by advanced cardiovascular conditions such as focal thickening and arterial plaques.

Impact of the demand for 'proxy assent' on recruitment to a randomised controlled trial of vaccination testing in care homes.

Legal frameworks are in place to protect those who lack the capacity to consent to research, such as the Mental Capacity Act in the UK. Assent is sought instead from a proxy, usually a relative. However, the same legislation may, perversely, affect the welfare of those who lack capacity and of others by hindering the process of recruitment into otherwise potentially beneficial research. In addition, the onus of responsibility is moved from those who know most about the study (ie, the scientific community) to those who know less (the proxies). In this paper, we describe the characteristics of a sample at different stages of the recruitment process of an influenza vaccine-based randomised control trial in elderly care home residents (the FEVER study). 62% (602/968) of potential subjects lacked capacity but only 29% (80/277) of those actually randomised. Older age, being female and living in an Elderly Mentally Ill care home were the only variables associated with lacking capacity. Considering this was a study based in a care home setting where the prevalence of dementia approximates 80%, the trial, like many others, was thus significantly biased. We believe that difficulties seeking proxy assent contributed significantly to this problem. Further thought should be given to how assent to enter research for those who lack capacity should be provided, and we suggest avenues for further discussion such as independent risk/benefit expert panels.

A novel framework for cellular tracking and mitosis detection in dense phase contrast microscopy images.

The aim of this paper is to detail the development of a novel tracking framework that is able to extract the cell motility indicators and to determine the cellular division (mitosis) events in large time-lapse phase-contrast image sequences. To address the challenges induced by nonstructured (random) motion, cellular agglomeration, and cellular mitosis, the process of automatic (unsupervised) cell tracking is carried out in a sequential manner, where the interframe cell association is achieved by assessing the variation in the local cellular structures in consecutive frames of the image sequence. In our study, a strong emphasis has been placed on the robust use of the topological information in the cellular tracking process and in the development of targeted pattern recognition techniques that were designed to redress the problems caused by segmentation errors, and to precisely identify mitosis using a backward (reversed) tracking strategy. The proposed algorithm has been evaluated on dense phase-contrast cellular data and the experimental results indicate that the proposed algorithm is able to accurately track epithelial and endothelial cells in time-lapse image sequences that are characterized by low contrast and high level of noise. Our algorithm achieved 86.10% overall tracking accuracy and 90.12% mitosis detection accuracy.

Buprenorphine vs methadone treatment: A review of evidence in both developed and developing worlds.

Heroin dependence is a major health and social problem associated with increased morbidity and mortality that adversely affects social circumstances, productivity, and healthcare and law enforcement costs. In the UK and many other Western countries, both methadone and buprenorphine are recommended by the relevant agencies for detoxification from heroin and for opioid maintenance therapy. However, despite obvious benefits due to its unique pharmacotherapy (eg, greatly reduced risk of overdose), buprenorphine has largely failed to overtake methadone in managing opioid addiction. The experience from the developing world (based on data from India) is similar. In this article we compare the advantages and disadvantages of the use methadone and buprenorphine for the treatment of opioid addiction from both a developed and developing world perspective; and explore some of the reasons why buprenorphine has not fulfilled the expectations predicted by many in the addictions field.

Convective tissue movements play a major role in avian endocardial morphogenesis.

Endocardial cells play a critical role in cardiac development and function, forming the innermost layer of the early (tubular) heart, separated from the myocardium by extracellular matrix (ECM). However, knowledge is limited regarding the interactions of cardiac progenitors and surrounding ECM during dramatic tissue rearrangements and concomitant cellular repositioning events that underlie endocardial morphogenesis. By analyzing the movements of immunolabeled ECM components (fibronectin, fibrillin-2) and TIE1 positive endocardial progenitors in time-lapse recordings of quail embryonic development, we demonstrate that the transformation of the primary heart field within the anterior lateral plate mesoderm (LPM) into a tubular heart involves the precise co-movement of primordial endocardial cells with the surrounding ECM. Thus, the ECM of the tubular heart contains filaments that were associated with the anterior LPM at earlier developmental stages. Moreover, endocardial cells exhibit surprisingly little directed active motility, that is, sustained directed movements relative to the surrounding ECM microenvironment. These findings point to the importance of large-scale tissue movements that convect cells to the appropriate positions during cardiac organogenesis.

Outcomes of laparoscopic donor nephrectomy in the presence of multiple renal arteries.

PURPOSE: Data of laparoscopic donor nephrectomy (LDN) with multiple renal arteries for donor and recipient outcomes were reviewed, with the aim of clarifying whether the laparoscopic approach is safe in the presence of multiple renal arteries. MATERIALS AND METHODS: All donor nephrectomies performed at our institution from 2004 to 2008 were reviewed retrospectively. Results were compared between LDN kidneys with multiple arteries and those with a single renal artery. RESULTS: Out of 171 donor nephrectomies, 21 (12%) were performed for kidneys with multiple renal arteries. All of the 150 (88%) donor nephrectomies in the single vessel group were performed laparoscopically. In the multiple artery group, 9 (43%) underwent an open procedure while 12 (57%) underwent a laparoscopic procedure. The warm ischemia time was longer in the multiple artery group than the single artery group, but the difference was not statistically significant (4.25±0.87 min vs. 4.12±0.95 min, respectively). Regarding transplant recipients, the vascular anastomosis time was similar in both groups (30±4.6 min vs. 29.5±3.7 min). The operative blood loss in the transplant recipients was significantly more in the multiple artery group compared to the single artery group (339±292 ml and 130.7±44.8 ml, respectively; P=0.03). The recipient renal function was similar for both the groups at postoperative day 7, 1 month, and at 1 year. CONCLUSION: The data support the fact that the laparoscopic approach to donor nephrectomy in the presence of multiple renal arteries can be performed safely with adequate laparoscopic experience.

Life before and after residents: subjective reports on quality of life from urologists since inception of a new residency program.

BACKGROUND: It is difficult to determine the effect of a residency program on the life of staff urologists. The objective of this study was to obtain subjective reports from urologists who have practiced before and after the implementation of a training program on how it affects their careers in 5 spheres: education, job-stress, free time, financial life and subjective quality of life. METHODS: We asked urologists from McMaster University to complete a questionnaire to quantify how their current experiences have changed compared to the pre-residency program era on a balanced 7-point scale (4 = neutral). RESULTS: The response rate was 100% (9/9). Eight of the 9 urologists (89%) reported they would implement the program again if they could rewind the clock. Eight of 9 reported their overall career-related quality of life improved, with an average rating of 5.1 on the 7-point scale. The quality of continuing education was the most positive ranking at 5.4 followed by job stress at 5.2. The outcomes measured below 4 (neutral) were earning potential at 3.8 and ability to engage in pastimes at 3.4. Earning potential was clustered tightly around neutral, with 7 of the 9 respondents reporting no change. The largest standard deviation, corresponding to the most disagreement, was in their ability to engage in pastimes. CONCLUSION: Even with a mild decrease in earning potential and increased job stress, McMaster urologists feel their quality of life and continuing education have improved since the program's implementation; these urologists are almost uniformly happy they started a residency teaching program at their centre.

A novel model-based 3D +time left ventricular segmentation technique.

A common approach to model-based segmentation is to assume a top-down modelling strategy. However, this is not feasible for complex 3D +time structures, such as the cardiac left ventricle, due to increased training requirements, aligning difficulties and local minima in resulting models. As our main contribution, we present an alternate bottom-up modelling approach. By combining the variation captured in multiple dimensionally-targeted models at segmentation-time we create a scalable segmentation framework that does not suffer from the "curse of dimensionality." Our second contribution involves a flexible contour coupling technique that allows our segmentation method to adapt to unseen contour configurations outside the training set. This is used to identify the endo- and epicardium contours of the left ventricle by coupling them at segmentation-time, instead of at model-time. We apply our approach to 33 3D +time cardiac MRI datasets and perform comprehensive evaluation against several state-of-the-art works. Quantitative evaluation illustrates that our method requires significantly less training than state-of-the-art model-based methods, while maintaining or improving segmentation accuracy.