Kidney targeting and puncturing during percutaneous nephrolithotomy: recent advances and future perspectives

Background and Purpose: Precise needle puncture of the kidney is a challenging and essential step for successful percutaneous nephrolithotomy (PCNL). Many devices and surgical techniques have been developed to easily achieve suitable renal access. This article presents a critical review to address the methodologies and techniques for conducting kidney targeting and the puncture step during PCNL. Based on this study, research paths are also provided for PCNL procedure improvement. Methods: Most relevant works concerning PCNL puncture were identified by a search of Medline/PubMed, ISI Web of Science, and Scopus databases from 2007 to December 2012. Two authors independently reviewed the studies. Results: A total of 911 abstracts and 346 full-text articles were assessed and discussed; 52 were included in this review as a summary of the main contributions to kidney targeting and puncturing. Conclusions: Multiple paths and technologic advances have been proposed in the field of urology and minimally invasive surgery to improve PCNL puncture. The most relevant contributions, however, have been provided by the applicationofmedical imaging guidance, newsurgical tools,motion tracking systems, robotics, andimage processing and computer graphics. Despite the multiple research paths for PCNL puncture guidance, no widely acceptable solution has yet been reached, and it remains an active and challenging research field. Future developments should focus on real-time methods, robust and accurate algorithms, and radiation free imaging techniques

Multi-agent systems and birtual producers in electronic marketplaces

This paper presents an agent-based simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviours, preference models and pricing algorithms, considering user risk preferences. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions. In the simulated market agents interact in several different ways and may joint together to form coalitions. In this paper we address multi-agent coalitions to analyse Distributed Generation in Electricity Markets

Caffeine effect on mortality and oviposition in successive generations of Aedes aegypti

OBJECTIVE: Previous experiments showed that caffeine blocks the development of Aedes aegypti (Diptera, Culicidae) in the larval stage, consequently inhibiting the production of adults. This study aimed at obtaining data suggestive of caffeine resistance by these mosquitoes. METHODS: Experiments were carried out in successive generations to assess adult production from eggs laid in previous generation and oviposition rate in every generation using 200 and 500 µg/mL caffeine. Tap water was used as control. Experiments were conducted in the city of São José do Rio Preto, Southeastern Brazil between 2002 and 2005. Statistical tests consisted of exploratory data analysis and smoothing algorithms. RESULTS: Increasing reduction in productivity of adults occurred among generations at both caffeine concentrations but the differences were only significant at 200µg/mL caffeine. As for the oviposition rate, there was a decrease in the mean number of eggs per female over generations at both caffeine concentrations. CONCLUSIONS: There was no evidence of caffeine resistance over generations. The study results corroborate caffeine as an alternative as an important Ae. Aegypti control agent to avoid resistance.

Comparison of the pencil beam convolution algorithm and the analytical anisotropic algorithm in breast tumors

The calculation of the dose is one of the key steps in radiotherapy planning1-5. This calculation should be as accurate as possible, and over the years it became feasible through the implementation of new algorithms to calculate the dose on the treatment planning systems applied in radiotherapy. When a breast tumour is irradiated, it is fundamental a precise dose distribution to ensure the planning target volume (PTV) coverage and prevent skin complications. Some investigations, using breast cases, showed that the pencil beam convolution algorithm (PBC) overestimates the dose in the PTV and in the proximal region of the ipsilateral lung. However, underestimates the dose in the distal region of the ipsilateral lung, when compared with analytical anisotropic algorithm (AAA). With this study we aim to compare the performance in breast tumors of the PBC and AAA algorithms.

Optimal tuning of fractional controllers using genetic algorithms

This study addresses the optimization of fractional algorithms for the discrete-time control of linear and non-linear systems. The paper starts by analyzing the fundamentals of fractional control systems and genetic algorithms. In a second phase the paper evaluates the problem in an optimization perspective. The results demonstrate the feasibility of the evolutionary strategy and the adaptability to distinct types of systems.

Application of fractional algorithms in the control of a robotic bird

In this paper, it is studied the dynamics of the robotic bird in terms of time response and robustness. It is analyzed the wing angle of attack and the velocity of the bird, the tail influence, the gliding flight and the flapping flight. The results are positive for the construction of flying robots. The development of computational simulation based on the dynamic of the robotic bird should allow testing strategies and different algorithms of control such as integer and fractional controllers.

Immunohistochemical molecular phenotypes of gastric cancer based on SOX2 and CDX2 predict patient outcome

Background Gastric cancer remains a serious health concern worldwide. Patients would greatly benefit from the discovery of new biomarkers that predict outcome more accurately and allow better treatment and follow-up decisions. Here, we used a retrospective, observational study to assess the expression and prognostic value of the transcription factors SOX2 and CDX2 in gastric cancer. Methods SOX2, CDX2, MUC5AC and MUC2 expression were assessed in 201 gastric tumors by immunohistochemistry. SOX2 and CDX2 expression were crossed with clinicopathological and follow-up data to determine their impact on tumor behavior and outcome. Moreover, SOX2 locus copy number status was assessed by FISH (N = 21) and Copy Number Variation Assay (N = 62). Results SOX2 was expressed in 52% of the gastric tumors and was significantly associated with male gender, T stage and N stage. Moreover, SOX2 expression predicted poorer patient survival, and the combination with CDX2 defined two molecular phenotypes, SOX2+CDX2- versus SOX2-CDX2+, that predict the worst and the best long-term patients’ outcome. These profiles combined with clinicopathological parameters stratify the prognosis of patients with intestinal and expanding tumors and in those without signs of venous invasion. Finally, SOX2 locus copy number gains were found in 93% of the samples reaching the amplification threshold in 14% and significantly associating with protein expression. Conclusions We showed, for the first time, that SOX2 combined with CDX2 expression profile in gastric cancer segregate patients into different prognostic groups, complementing the clinicopathological information. We further demonstrate a molecular mechanism for SOX2 expression in a subset of gastric cancer cases.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Human cutaneous leishmaniasis caused by Leishmania (Viannia) braziliensis in Santiago del Estero, Argentina: identification of parasites by monoclonal antibodies and isoenzymes

Diagnostic and parasite characterization and identification studies were carried out in human patients with cutaneous leishmaniasis lesions in Santiago del Estero, Northern Province of Argentina. Diagnostic procedures were biopsies of lesions for smears and inoculations in hamster, needle aspirations of material from ulcers for "in vitro" cultures. Immunodiagnostic techniques applied were IFAT-IgG and Montenegro skin test. Primary isolation of eight stocks of leishmanial parasites was achieved from patients with active lesions. All stocks were biologically characterized by their behaviour in hamster, measurements of amastigote and promastigotes and growth "in vitro". Eight stocks were characterized and identified at species level by their reactivity to a cross-panel of sub-genus and specie-specific Monoclonal Antibodies through an Indirect Immunofluorescence technique and a Dot-ELISA. We conclude from the serodeme analysis of Argentina stocks that: stocks MHOM/AR/92/SE-1; SE-2; SE-4; SE-8; SE-8-I; SE-30; SE-34 and SE-36 are Leishmania (Viannia) braziliensis. Three Leishmania stocks (SE-1; SE-2 and SE-30) did not react with one highly specie-specific Monoclonal Antibody (Clone: B-18, Leishmania (Viannia) braziliensis marker) disclosing two serodeme group patterns. Five out of eight soluble extracts of leishmanial promastigotes were electrophoresed on thin-layer starch gels and examined for the enzyme MPI, Mannose Phosphate Isomerase; MDH, Malate Dehydrogenase; 6PGD, 6 Phosphogluconate Dehydrogenase; NH, Nucleoside Hydrolase, 2-deoxyinosinc as substrate; SOD, Superoxide Dismutase; GPI, Glucose Phosphate Isomerase and ES, Esterase. From the isoenzyme studies we concluded that stocks: MHOM/AR/92/SE-1; SE-2; SE-4; SE-8 and SE-8-I are isoenzymatically Leishmania (Viannia) braziliensis. We need to analyze more enzymes before assigning them to a braziliensis zymodeme.

Characterization of Pinus spp. needles by gas chromatography and mass spectrometry: Application to plant-insect interactions

Dissertação apresentada para obtenção do Grau de Doutor em Ciências do Ambiente pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecn

Global and Partitioned Multiprocessor Fixed Priority Scheduling with Deferred Pre-emption

This article introduces schedulability analysis for global fixed priority scheduling with deferred preemption (gFPDS) for homogeneous multiprocessor systems. gFPDS is a superset of global fixed priority pre-emptive scheduling (gFPPS) and global fixed priority non-pre-emptive scheduling (gFPNS). We show how schedulability can be improved using gFPDS via appropriate choice of priority assignment and final non-pre-emptive region lengths, and provide algorithms which optimize schedulability in this way. Via an experimental evaluation we compare the performance of multiprocessor scheduling using global approaches: gFPDS, gFPPS, and gFPNS, and also partitioned approaches employing FPDS, FPPS, and FPNS on each processor.

CMV and HSV-2 myeloradiculitis in an HIV infected patient

While CMV myeloradiculitis is a known complication in AIDS patients with severe immunosuppression, HSV-2 necrotizing myeloradiculitis is rare and often associated with disabling a fatal outcome. We hereby describe a 46 year-old HIV infected patient with profound and sustained immunosuppression who presented with an acute ascending paraparesis and urinary retention. Lumbar spine MRI showed contrast enhancement at the conus medullaris and cauda equine, and both CMV and HSV-2 CSF PCR were positive. Despite treatment, the patient died 20 days later. We review the main diagnostic and therapeutic aspects of herpes virus associated myeloradiculitis and discuss the approach in similar cases.

Ambulatory Hysteroscopy Results Post-Menopause: Comparative Study Between Patients With and Without Metrorrhagia

Our objective was to compare the results of ambulatory hysteroscopy in postmenopausal women with and without uterine bleeding. A retrospective descriptive study was carried out on 236 women who were at least 2 years into the menopause, who were not undergoing hormone treatment and who had had abnormal pelvic ultrasound results. Of these women, 150 were asymptomatic and 86 reported haemorrhage. Diagnostic and operative outpatient hysteroscopy was performed between January 2002 and December 2003. There was no difference between the two groups regarding age of patients, age of menopause and presence of at least one of the risk factors for endometrial carcinoma evaluated, although obesity was more frequent in the symptomatic group. Abnormal ultrasound results for these women corresponded in the majority of cases to intracavitary disease, and the absence of organic endometrial pathology was 9.3% vs 11.3% in each group. The more frequent pathology was benign endometrial polyps (64% in bleeding patients and 84.7% in asymptomatic ones). Endometrial carcinoma was diagnosed in 23.3% of women with metrorrhagia and in 1.3% of asymptomatic women. We diagnosed 2.6% of malignancy inside polyps. Hysteroscopy results were confirmed by histology in 90.3% of cases. See and treat in one session was achieved in 91% of benign endometrial polyps. Ambulatory hysteroscopy has high sensitivity and specificity for intracavitary pathology and high tolerability and safety. See and treat in one session can be achieved in the majority of lesions with indication for excision. These results make us advise our menopausal patients with abnormal uterine bleeding to undergo diagnostic hysteroscopy complemented with biopsy.

Clinical and evolutionary characteristics of four patients with pulmonary histoplasmosis reported in the Paraíba Paulista Valley region

The type of pulmonary histoplasmosis presents limited lesions to the lungs, with symptoms that are clinically and radiological similar to chronic pulmonary tuberculosis. This paper describes the clinical features of four cases of pulmonary histoplasmosis. Aspects of diagnostic and clinical, epidemiological, laboratorial and imaging exams are discussed, in addition to the clinical status of the individuals five years after disease onset. The treatment of choice was oral medication, following which all the patients improved. It is important to understand the clinical status and the difficulties concerning the differential diagnosis of histoplasmosis, to assist the proper indication of cases, thus reducing potential confusion with other diseases.

The characteristics, clinical manifestations and outcomes of pandemic influenza A (H1N1) 2009 in the elderly

IntroductionThe objetctive of this study was to evaluate the 2009 Pandemic Influenza A (H1N1) in the elderly and identify the clinical characteristics, mortality and prognostic factors of the infection in these patients.MethodsThis was an observational, retrospective study. Data were collected from the National Notifiable Diseases (SINAN), from the Brazilian Ministry of Health. Only patients 60 years old or more that had laboratory confirmed infections were included. The socio-demographic and clinical variables and outcomes were evaluated to compare mortality rates in the presence or absence of these factors.ResultsWe included 93 patients in the study, 16.1% of whom died. The symptoms of cough and dyspnea, the use of the antiviral oseltamivir, influenza vaccine and comorbidities influenced the outcomes of cure or death. Chest radiography can aid in diagnosis.ConclusionsAlthough relatively few elderly people were infected, this population presented high lethality that can be justified by the sum of clinical, physical and immunological factors in this population. Treatment with oseltamivir and vaccination against seasonal influenza have significantly reduced rates of hospitalization and mortality.