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Objetivo. Elaborar un mapa de publicaciones donde se visualicen las investigaciones científicas espacializadas a través de puntos de coordenadas geográficas en el departamento de Loreto, de forma preliminar con publicaciones de la revista Folia Amazónica.Método. Se aplicaron técnicas cartográficas y bibliométricas para analizar 255 artículos científicos publicados entre los años 1988 y 2016 en la revista Folia Amazónica.Resultados. Las publicaciones estuvieron centralizadas en el área de influencia de la carretera Iquitos – Nauta, Jenaro Herrera, cuenca baja y media del río Nanay y sector izquierdo de la cuenca del río Marañón. La red científica generada a través de la revista Folia Amazónica evidenció una figura “no conexa”. Las afiliaciones institucionales correspondieron a 71 entidades, 12 países y 386 autores, donde Perú y Francia tuvieron mayor producción científica, en tanto Estados Unidos tuvo mayor cantidad de instituciones afiliadas a la revista Folia Amazónica después del Perú.Conclusiones. El mapa de publicaciones representa una gran herramienta de investigación con proyección de desarrollo y gestión científica. La integración con otras revistas permitirá orientar proyectos que coadyuven al desarrollo del departamento de Loreto y en un futuro en el ámbito amazónicos.

Although the expression of several connexins has been reported in the central nervous system, only Cx36 and Cx45 have been undoubtedly demonstrated as part of electrical synapses. Dopamine neurons of the substantia nigra pars compacta (SNc), whose electrical activity is important for goal directed behavior, learning, working memory, reward and modulation of motor activity, communicate with other Dopamine neurons through electrical synapses. However, the expression profile of connexins in the SNc has only been determined in the rat. Considering that electrical synchronization of Dopamine neurons could have important roles in the modulation of levels of Dopamine released both locally and in the areas of innervation, and therefore in the functioning of Dopamine circuits, we decided to determine the expression profile of connexins in freshly isolated cells from SNc of adult mice. Using single cell RT-PCR, we show that a subset of DA neurons, and neurons of glutamate and GABA phenotypes, express Cx45 as well Cx26, Cx30, Cx31.1, Cx32 and Cx43, but not Cx36. Thus our results suggest that in adult mice, Cx45 is the connexin used by some of DA neurons of the SNc to be electrically coupled. Our results also suggest that glutamate and GABA neurons of the SNc, could also be electrically coupled with DA neurons. Despite the inability of the other connexins to form functional gap junctions in neurons, the expression of Cx26, Cx30, Cx32 and Cx43 may be the reflect of the ability of the neurons of SNc to release neuromodulators through hemichannel activity.

In this article, parametric and nonparametric statistical inference analysis of a set the measurements of air pollution because of PM2.5 concentrations was performed. The research work was carried out in an urban park in Quito, Ecuador. Specifically, the park that was chosen to perform the analysis was La Carolina Park. The analysis carried out here was aimed at obtaining the statistical models for parts of this urban park under study and some of its border streets. Furthermore, the park and its border streets were modeled as random variables that were finally classified according to the amount of PM2.5 concentration levels they carry. This classification was performed by using a method based on both Friedman’s test and the categories of the index of air quality of Quito. The results of this article showed that air pollution levels because of PM2.5 concentrations in La Carolina Park are not in alert level. The worst case, considering the analysis tools used in this article, is that one of the streets that border the park is in caution level. The other streets and parts of the park that were analyzed are either in a desirable level or in an acceptable level. Furthermore, in this article, it has been shown that as pedestrian and temporary residents move further away from the trees and vegetation of the park, the level of exposure to PM2.5 concentrations that they experience is higher.

Rapid population growth, urbanization and motorization have brought about secondary effects that have gradually damaged the atmosphere, whose importance is vital for both the survival of all living beings and the climate balance. In this sense, air pollution is a problem that affects current society and is much more critical in developing countries. In this context, in the present paper non-parametric statistical inference techniques are used to carry out the analysis of measurements of health concerning fine particulate matter concentration, PM2.5, in an urban park of Quito, Ecuador. In short, the data collected during the measurements were stored in random variables and the Kruskal-Wallis test was used to test if these random variables come from populations with identical distributions. Also, the Wilcoxon signed rank test was used to test if the numerical values collected in the samples of the random variables of interest represent a level of contamination that could be dangerous for human beings. The experimental results show that urban parks and, specifically, trees are a natural filter between the pollution generated in the road and the center of the park. Therefore, the role of trees in the face of vehicular pollution will depend on two variables: the amount and compactness of the vegetation, and the emission levels recorded in the border roads.

In this paper, a robust analysis of SO 2 concentration measurements taken at the Belisario air quality monitoring station, Quito, Ecuador is carried out. The analyzed data contain 12 years of measurements, from January 2008 to December 2019. In addition, this set of measurements was decomposed into variables that represent each year, month, day of the week, and hour of the day in groups of two hours. For the analysis, classic, nonparametric and robust statistical methods were used, and the data were classified based on criteria established by the Quiteno Air Quality Index, taking confidence intervals into account. The results showed that the level of air pollution at the Belisario station due to the SO 2 concentration is acceptable. In addition, the trend in the level of SO 2 concentration decreased over the years studied, with a sharp drop from 2008 to 2012, then a small rise in 2013 and another fall until 2019, presenting decreasing oscillations that tend toward a desirable level of pollution. In this paper, it was shown that the air pollution at the Belisario station due to the concentration of SO 2 in the last 12 years is not harmful to humans, with the measurement precision provided by robust statistical methods. Therefore, it can be concluded that the measures that have been taken by the Quito city council over the last few years are yielding good results.

INTRODUCTION: There are currently more than 450 primary immune deficiency (PID) diseases, and about 7,000 rare diseases that together afflict around 1 in every 17 humans. Computational aids based on data mining and machine learning might facilitate the diagnostic task by extracting rules from large datasets and making predictions when faced with new problem cases. OBJECTIVE: In a proof-of-concept data mining study, we aimed to predict PID diagnoses with a supervised machine learning algorithm based on classification tree boosting. METHODS: Through a data query at the USIDNET registry we obtained a database of 2,396 patients with common diagnoses of PID, including their clinical and laboratory features. We kept 12 diagnoses and 286 features that were included in the model. We used the XGBoost package with parallel tree boosting for the supervised classification model, and SHAP for variable importance interpretation, on Python v3.7. The patient database was split into training and testing subsets, and after boosting through gradient descent, the predictive model provides measures of diagnostic prediction accuracy and individual feature importance. To correct for imbalanced classification, after a baseline performance test, we used the Class Weighting Hyperparameter, or scale_pos_weight. RESULTS: The twelve PID diagnoses were CVID (1,098 patients), DiGeorge syndrome, Chronic granulomatous disease, Congenital agammaglobulinemia, ID not otherwise classified, Specific antibody deficiency, Complement deficiency, Hyper-IgM, Leukocyte adhesion deficiency, ectodermal dysplasia with immune deficiency, Severe combined immune deficiency, and Wiskott-Aldrich syndrome. For CVID, the model found an accuracy on the train sample of 0.80, with an area under the ROC curve (AUC) of 0.80, and a Gini coefficient of 0.60. In the test subset, accuracy was 0.76, AUC 0.75, and Gini 0.51. The positive feature value to predict CVID was highest for upper respiratory infections, asthma, autoimmunity and hypogammaglobulinemia. Features with the highest negative predictive value were high IgE, growth delay, abscess, lymphopenia, and congenital heart disease. For the rest of the diagnoses, accuracy stayed between 0.75 and 0.99, AUC 0.46-0.87, Gini 0.07-0.75, and LogLoss 0.09-8.55. See tables and figures.DISCUSSION: Clinicians should remember to consider the negative predictive features together with the positives. We are calling this a proof-of-concept study to continue with our explorations. A good performance is encouraging, and the feature importance might aid feature selection for future endeavors. In the meantime, we can learn from the rules derived by the model and build a user-friendly decision tree to generate differential diagnoses.

A practical example of a theory-guided data science case study is presented to evaluate the potential of the Diyab formation, an Upper Jurassic interval, source rock of some of the largest reservoirs in the Arabian Peninsula. A workflow base on a three-step approach combining the physics of logging tool response and a probabilistic machine-learning algorithm was undertaken to evaluate four wells of the prospect. At first, a core-calibrated multi-mineral model was established on a concept well for which an extensive suite of logs and core measurements had been acquired. To transfer the knowledge gained from the latter physics-driven interpretation onto the other data-scarce wells, the relationship between the output rock and fluid volumes and their input log responses was then learned by means of a Gaussian Process Regression (GPR). Finally, once trained on the key well, the latter probabilistic algorithm was deployed on the three remaining wells to predict reservoir properties, quantify resource potential and estimate volumetric-related uncertainties. The physics-informed machine-learning approach introduced in this work was found to provide results which matches with the majority of the available core data, while discrepancies could generally be explained by the occurrence of laminations which thickness are under the resolution of nuclear logs. Overall, the GPR approach seems to enable an efficient transfer of knowledge from data-rich key wells to other data-scarce wells. As opposed to a more conventional formation evaluation process which is carried out more independently from the key well, the present approach ensures that the final petrophysical interpretation reflects and benefits from the insights and the physics-driven coherency achieved at key well location.

This paper analyzes 12 years of tropospheric ozone (O3) concentration measurements using robust techniques. The measurements were taken at an air quality monitoring station called Belisario, which is in Quito, Ecuador, the data collection time period was 1 January 2008 to 31 December 2019, and the measurements were carried out using photometric O3 analyzers. Here, the measurement results were used to build variables that represented hours, days, months, and years, and were then classified and categorized. The index of air quality (IAQ) of the city was used to make the classifications, and robust and nonrobust confidence intervals were used to make the categorizations. Furthermore, robust analysis methods were compared with classical methods, nonparametric methods, and bootstrap-based methods. The results showed that the analysis using robust methods is better than the analysis using nonrobust methods, which are not immune to the influence of extreme observations. Using all of the aforementioned methods, confidence intervals were used to both establish and quantify differences between categories of the groups of variables under study. In addition, the central tendency and variability of the O3 concentration at Belisario station were exhaustively analyzed, concluding that said concentration was stable for years, highly variable for months and hours, and slightly changing between the days of the week. Additionally, according to the criteria established by the IAQ, it was shown that in Quito, the O3 concentration levels during the study period were not harmful to human health.

This paper presents, in a descriptive and didactic way, the construction of a robotic platform, detailing the necessary components for this and the assembly process. This is done in order that students with limited engineering fundamentals, can build the model of a robotic platform without much difficulty and that this also helps them to implement control, navigation and monitoring algorithms that they will later know in the development of their studies.

In this paper, a practical comparison is performed between the use of the principal component analysis method (PCA) and the multidimensional scaling method (MDS) for image classification applications, where the images under study are contaminated using different tools. Here, characteristics of these images are obtained and, according to criteria for measuring the distance between the images, their classification is carried out. For the case under study, it can be said that the classification using PCA performed better than the classification using MDS.

In this chapter, the almost periodicity of the first principal components is used to carry out the reconstruction of images. First, the principal component analysis technique is applied to an image. Then, the periodicity of its principal components is analyzed. Next, this periodicity is used to build periodic vectors of the same length of the original principal components, and finally, these periodic vectors are used to reconstruct the original image. The proposed method was compared against the JPEG (Joint Photographic Experts Group) compression technique. The mean square error and peak signal-to-noise ratio were used to perform the above-mentioned comparison. The experimental results showed that the proposed method performed better than JPEG, when the original image was reconstructed using the principal components modified by periodicity.

In the present paper, the almost periodicity of the first principal components is studied, with the aim of being able to use less information in order to obtain acceptable reconstructions of compressed images. The results of this study show that by working with the periodic principal components of images under analysis, it is possible to obtain an additional reduction to that obtained by using the original principal components. Specifically, it is shown that if the principal components that are considered periodic are replaced by their period plus a trend, it can be said that the reconstruction achieved using these periodic principal components is very close to the reconstruction achieved using the original principal components. In the present paper, the almost periodicity of the first principal components is studied, with the aim of being able to use less information in order to obtain acceptable reconstructions of compressed images. The results of this study show that by working with the periodic principal components of images under analysis, it is possible to obtain an additional reduction to that obtained by using the original principal components. Specifically, it is shown that if the principal components that are considered periodic are replaced by their period plus a trend, it can be said that the reconstruction achieved using these periodic principal components is very close to the reconstruction achieved using the original principal components.

In this paper, a method for image classification based on multidimensional scaling (MDS) is presented. Once the image has been fragmented into smaller cells and the wavelet decomposition has also been performed, up to a certain level, statistics are obtained from the new cells with the aim of constructing vectors of characteristics of the original image. Then, these vectors are grouped in a matrix and the MDS of said matrix is performed. Finally, the principal coordinates of the MDS indicate the proximity between vectors and, therefore, between images.

There is a wide variety of methods for the analysis of textures and the extraction of image characteristics based on wavelet decomposition. The objective of this paper is to find a vector of characteristics of each image and to determine a classification among them using principal component analysis. The procedures presented here are for classifying test images. After fragmenting the image, a wavelet decomposition of the fragmented images is performed both in scale and in orientation. To characterize the images, statistics of marginal and joint distributions based on local neighborhoods of spatial, orientation and scale type are used.

Several challenges are associated with reservoir characterization of organic-rich, unconventional plays, most significantly with estimating producible hydrocarbons and identifying sweet spots for horizontal wells and subsequent stimulation. This paper illustrates the data integration approach from the Shilaif member and the important factors for the hydraulic fracturing simulations and execution. The Shilaif member consists of a succession of argillaceous limestone, mostly fine-grained packstones and wackestones with subordinate calcareous shales in the lower part. The complex carbonate lithology and fabric, combined with low porosity and the requirement to evaluate total organic carbon, presents a challenge to conventional logs and evaluation. Low permeability and productivity dictate the requirement to stimulate the wells effectively. Thorough integration of advanced and conventional log data (resistivity, neutron/density, dielectric, advanced acoustic, spectroscopy, nuclear magnetic resonance (NMR), and images) with core data and mud logs plays a critical role in the evaluation and development of these organic-rich reservoirs. Extensive data acquisition was planned with a wireline suite that included resistivity/density/neutron/spectral gamma ray; acoustic logs; acoustic image; NMR; advanced elemental spectroscopy; and dielectric technologies to characterize the hydrocarbon potential of organic-rich rock within the Shilaif member. The same suite of logs are critical for hydraulic fracturing simulations and play a heavy role when executing and pressure-matching the fracture geometry. Lithology and porosity from neutron/density logs are refined with NMR and spectroscopy to enable accurate evaluation of total organic carbon (TOC) and volumes. The advanced elemental spectroscopy data provided the mineralogy, the amount of carbon in the rock, and consequently the associated organic carbon within the Shilaif member. The NMR technology provided lithology-independent total porosity. The difference between the NMR and the density techniques provides accurate information about organic matter. NMR technology in this present case study was used to identify and differentiate the organic matter and hydrocarbon presence within the Shilaif member. Acoustic and image logs were used to evaluate the geomechanical properties that enable stimulation design to maximize the drainage while remaining within the boundaries of the reservoir. Accurate calibration of the stress profiles from core data assured the stimulation design was operationally achievable within pressure specifications and bounding formations. Detailed knowledge of natural fracture networks was critical to building an accurate geomechanical model. A complete workflow from formation evaluation to selection of interval to stimulate the Shilaif formation will be presented and used for future well development. The data integration work illustrated in the paper is a key for unconventional reservoir characterization that enabled identification of the sweet spots for horizontal wells and the successful hydraulic fracturing in the organic rich rocks of the Shilaif member.

In this chapter, an introduction to the basics of principal component analysis (PCA) is given, aimed at presenting PCA applications to image compression. Here, concepts of linear algebra used in PCA are introduced, and PCA theoretical foundations are explained in connection with those concepts. Next, an image is compressed by using different principal components, and concepts such as image dimension reduction and image reconstruction quality are explained. Also, using the almost periodicity of the first principal component, a quality comparative analysis of a compressed image using two and eight principal components is carried out. Finally, a novel construction of principal components by periodicity of principal components has been included, in order to reduce the computational cost for their calculation, although decreasing the accuracy.

The frequency-domain analysis of the performance of a wind turbine that is placed in a mountainous complex terrain is presented. The variables of interest were the wind speed and the active power of the wind turbine, and the experimental data for the analysis were collected for three months with a sampling time of 10 minutes. The power spectral density (PSD) of the wind speed and of the active power of the wind turbine were obtained for every day of the three months under analysis, and this information was used to estimate the magnitude squared of the frequency response of the system and to estimate the average value of the magnitude squared of the frequency response. Then, the energy distribution of the wind turbine over a range of frequencies was analysed.

Excess fluid leak-off, a challenge in Kuwait's naturally fractured tight carbonate formations, can compromise post-fracture productivity. Past acid fracture treatments, both for moderate and high temperature formations failed to generate the long differently etched fracture due to excessive leak-off. Treating zones with multiple perforated intervals in a single stage, particularly in pay zones with long heterogeneous rock properties can result in non optimal stimulation. Therefore, a new approach was developed with proven success to enhance fracture conductivity and overall production by efficient control of fluid leak-off. This novel approach incorporates the use of far-field and near-wellbore diverting systems into the acid fracture design. These solid particulate diverters (SPD) include low and high temperature systems that provide enhanced near-wellbore diversion in both case and open-hole applications. The SPD are designed to bridge across perforations and fractures in the higher permeability zones, diverting the stimulation fluid into lower permeability zones. A smaller sized multi-modal distribution of SPD controls the fluid in narrower natural fractures and wormholes, deepening penetration of the stimulation fluid along the entire fracture length. The SPD agents are fully degradable and do not contribute to permeability loss of the created fracture or the perforated interval when production starts. The production of the wells where the SPD agents were applied were higher in comparison to expected production of offset wells where non-acid crosslinked fracturing pad stages alternated with gelled or emulsified acid, and visco-elastic surfactant (VES) slugs. Both crosslinked fracturing pads, VES and emulsified acid slugs do not effectly control live acid leak off. Two case histories, documenting successes where this new approach to acid fracturing has been applied in the Tuba and Middle Marrat formations, have superior production results that correspond to enhanced fracture geometry.

Success and failure of hydraulic fracturing is driven by a number of factors including operational procedures, fluid dynamics and the geomechanical response of the reservoir. Thus, the complex nature of the hydraulic fracturing process makes it difficult to correlate its success or failure to one of the many factors alone. However, in this paperwe demonstrate – based on real experiences–some successes and failures that show strong evidence of being driven by geomechanics aspects alone. The unsuccessful hydraulic fracturing of a well can be often interpreted as a failure in terms of operational procedures. However, there are cases where the unfavorable geomechanical setting plays the dominant role on the fracturing success. Modeling such complex processes can be very time consuming and costly, especially, if fluid dynamics of the pumped fluids is integrated into the calculations. One suggested way forward is to modestly simplify the world of (rock-) physics and hence focus on the problem down to the main contributors of the processes while still reaching the desired and representative result. In this work we focused on stress and pressure changes around the wellbore and perforations, that have strong influence on the end result of fracturing. In four wells that were hydraulically fractured, we observed two wells experiencing successful breakdown (and propagation), whereas the other two wells could not be fractured. Optimal application of analyticalgeomechanical modeling was more than sufficient to prove that the major cause for the failure in formation breakdown was stress field changes around the well bore and perforations in coherence with the rockmechanical properties. Thus, the specific geomechanical setting has led to an elevated breakdown pressure that was too high to be achieved by the horse powerof the pressure pumping equipment. At the same time the model also explains the underlying reasons for the two successfully stimulated wells. This experience not only leadsto a geomechanical explanation for hydraulic fracturing- (formation breakdown) failure and success, but also to more customized recommendations for avoiding loss in pump horse power. This is of importance when limitations in pump- and completion pressures are a concern. From this experience we learn also about the influence of geomechanical drivers on well placement, completion strategy, perforation schemes and injection point selection.

Many neuropsychiatric conditions are primed or triggered by different types of stressors. The mechanisms through which stress induces neuropsychiatric disease are complex and incompletely understood. A ‘double hit’ hypothesis of neuropsychiatric disease postulates that stress induces maladaptive behavior in two phases separated by a dormant period. Recent research shows that the pleiotropic cytokine IL-6 is released centrally and peripherally following physical and psychological stress. In this article, we analyze evidence from clinics and animal models suggesting that stress-induced elevation in the levels of IL-6 may play a key role in the etiology of a heterogeneous family of hyperexcitable central conditions including epilepsy, schizophrenic psychoses, anxiety and disorders of the autistic spectrum. The cellular mechanism leading to hyperexcitable conditions might be a decrease in inhibitory/excitatory synaptic balance in either or both temporal phases of the conditions. Following these observations, we discuss how they may have important implications for optimal prophylactic and therapeutic pharmacological treatment.

Having an accurate model of the power curve of a wind turbine allows us to better monitor its operation and planning of storage capacity. Since wind speed and direction is of a highly stochastic nature, the forecasting of the power generated by the wind turbine is of the same nature as well. In this paper, a method for obtaining a robust confidence band containing the power curve of a wind turbine under test conditions is presented. Here, the confidence band is bound by two curves which are estimated using parametric statistical inference techniques. However, the observations that are used for carrying out the statistical analysis are obtained by using the binning method, and in each bin, the outliers are eliminated by using a censorship process based on robust statistical techniques. Then, the observations that are not outliers are divided into observation sets. Finally, both the power curve of the wind turbine and the two curves that define the robust confidence band are estimated using each of the previously mentioned observation sets.

Ablating or functionally compromising sets of sensory neurons has provided important insights into peripheral modality-specific wiring in the somatosensory system. Inflammatory hyperalgesia, cold pain, and noxious mechanosensation have all been shown to depend upon Na v 1.8-positive sensory neurons. The release of fast-acting neurotransmitters, such as glutamate, and more slowly released neuropeptides, such as substance P (SP), contribute to the diversified responses to external stimuli. Here we show that deleting Vglut2 in Na v 1.8 Cre -positive neurons compromised mechanical pain and NGF-induced thermal hyperalgesia, whereas tactile-evoked sensation, thermal, formalin-evoked, and chronic neuropathic pain were normal. However, when Vglut2 f/f ;Na v 1.8 Cre mice were injected with a SP antagonist before the formalin test, the second phase pain response was nearly completely abolished, whereas in control mice, the pain response was unaffected. Our results suggest that VGLUT2-dependent signaling originating from Na v 1.8-positive neurons is a principal sensing mechanism for mechanical pain and, together with SP, inflammatory pain. These data define sets of primary afferents associated with specific modalities and provide useful genetic tools with which to analyze the pathways that are activated by functionally distinct neuronal populations and transmitters.

Impairments in axonal dopamine release are associated with neurological disorders such as schizophrenia and attention deficit hyperactivity disorder and pathophysiological conditions promoting drug abuse and obesity. The D2 dopamine autoreceptor (D2-AR) exerts tight regulatory control of axonal dopamine (DA) release through a mechanism suggested to involve K(+) channels. To evaluate the contribution of Kv1 voltage-gated potassium channels of the Shaker gene family to the regulation of axonal DA release by the D2-AR, the present study employed expression analyses, real time measurements of striatal DA overflow, K(+) current measurements and immunoprecipitation assays. Kv1.1, -1.2, -1.3, and -1.6 mRNA and protein were detected in midbrain DA neurons purified by fluorescence-activated cell sorting and in primary DA neuron cultures. In addition, Kv1.1, -1.2, and -1.6 were localized to DA axonal processes in the dorsal striatum. By means of fast scan cyclic voltammetry in striatal slice preparations, we found that the inhibition of stimulation-evoked DA overflow by a D2 agonist was attenuated by Kv1.1, -1.2, and -1.6 toxin blockers. A particular role for the Kv1.2 subunit in the process whereby axonal D2-AR inhibits DA overflow was established with the use of a selective Kv1.2 blocker and Kv1.2 knock-out mice. Moreover, we demonstrate the ability of D2-AR activation to increase Kv1.2 currents in co-transfected cells and its reliance on Gβγ subunit signaling along with the physical coupling of D2-AR and Kv1.2-containing channels in striatal tissue. These findings underline the contribution of Kv1.2 in the regulation of nigrostriatal DA release by the D2-AR and thereby offer a novel mechanism by which DA release is regulated.

Summary This paper provides a case history of the first hydraulic fracture on a subsea horizontal well in the Quissama formation, a low-permeability limestone reservoir in Campos basin, Brazil. The well was drilled in the direction of the expected fracture growth, thus evolving longitudinal fractures along the horizontal section. It is part of a research project to evaluate selective stimulation methods for subsea horizontal wells. Every aspect since the drilling, completion, and evaluation of the well was handled with the end in mind of hydraulically fracturing it in several stages. Hydraulically fracturing a horizontal well has become a more accepted practice in our industry. The procedures used in fracturing vertical wells must be taken into consideration when fracturing a horizontal well, to avoid refracturing work. Near-wellbore (NWB) problems are usually the main reason that fracturing work is not completed. This paper describes hydraulic-fracture treatments with detailed discussion on analysis of calibration tests, fluid-efficiency tests (FETs), and lessons learned. Results of the pumping work will be presented showing theory and how common practices played a key role for a successful application of techniques used. Laboratory tests performed with cores taken from the well itself, such as rock mechanics, proppant embedment, compaction measurements, and basic mineralogy, are presented. Production results will be presented and compared to conventional methods used on other subsea horizontal wells in Campos basin.

Mesencephalic dopamine (DA) neurons have been suggested to use glutamate as a cotransmitter. Here, we suggest a mechanism for this form of cotransmission by showing that a subset of DA neurons both in vitro and in vivo expresses vesicular glutamate transporter 2 (VGluT2). Expression of VGluT2 decreases with age. Moreover, when DA neurons are grown in isolation using a microculture system, there is a marked upregulation of VGluT2 expression. We provide evidence that expression of this transporter is normally repressed through a contact-dependent interaction with GABA and other DA neurons, thus providing a partial explanation for the highly restricted expression of VGluT2 in DA neurons in vivo. Our results demonstrate that the neurotransmitter phenotype of DA neurons is both developmentally and dynamically regulated. These findings may have implications for a better understanding of the fast synaptic action of DA neurons as well as basal ganglia circuitry.

Summary Most of the deepwater Campos Basin reservoirs, offshore Brazil, have unconsolidated formations that require sand control. Openhole gravel packing has proven to be the effective completion method of choice. The main objectives were to achieve low damage ratios, maximize completion efficiencies and reduce completion time. The project was conducted in one of the largest deepwater offshore fields in Brazil, Marlim Sul. This paper describes the learning curve and completion optimization obtained during planning, execution, and post-job analysis of six horizontal wells campaign with five water injectors and one oil producer. A combination of integrated service lines and experienced personnel were important to achieve excellent injectivity and productivity ratios. Several key operations from drilling to completion contributed to successfully complete these wells: openhole stability, drilling practices, drill-in fluids (DIFs), wellbore clean-up, filtration, completion fluids, completion tools, sand control, gravel-pack software simulator, and job execution. The efficiency of the service company was measured with a system of penalties and rewards allowing the operator to monitor and measure the progress of the learning curve on completion. As a final result, the partnership between the operator and the service companies contributed to a more economic completion cost by reducing completion time, and improving well performance.

To fracture unconventional reservoirs a different approach is required to be utilized. With the day-to-day advancement in fracturing technology more unconventional formations are being routinely fractured. But now and then we come across formations which challenge modern technologies and make us think out of box.This paper will describe the case history covering the planning and execution of one of the first "Acid-Frac" jobs in UAE. Being the first fracturing job in this formation, no previous stress gradient data was available nor were any leakoff test (LOT) or formation integrity test (FIT) results available at the planning stage. Various aspects of the reservoir and wellbore were considered, core samples of the formation were collected for testing and a 1-D mechanical earth model was developed. The paper will describe the steps taken to prepare the geological model along with the assumptions made. It will also discuss how the uncertainty of stress gradient was tackled while planning the job. Along with the unknown variables about formation, additional challenge was on the surface pressure limitation. Although tree-saver was planned to be used to protect the wellhead, the surface pressure limitation was due to wellbore completion. Based on the various unknowns and limitation on surface pressure, the job was designed. In addition to job planning, this paper will also discuss various lab tests which were conducted on the formation cores to optimally select acid fracturing fluid.Pre-job fracturing model was run with different scenarios which helped to predict if the job could be successfully pumped within the surface pressure limitation. Operation and equipment mobilization was optimized keeping in mind the uncertainties that were predicted. The paper will go into depth covering the different scenarios which were considered while planning the job, vital role of geological model and job execution. Although job execution did not go as per plan, paper will discuss various efforts which were made to establish injectivity and how this job helped in understanding the formation better learning lessons along the way which will be implemented in future campaigns.

Recent studies have proposed that glutamate corelease by mesostriatal dopamine (DA) neurons regulates behavioral activation by psychostimulants. How and when glutamate release by DA neurons might play this role remains unclear. Considering evidence for early expression of the type 2 vesicular glutamate transporter in mesencephalic DA neurons, we hypothesized that this cophenotype is particularly important during development. Using a conditional gene knock-out approach to selectively disrupt theVglut2gene in mouse DA neurons, we obtainedin vitroandin vivoevidence for reduced growth and survival of mesencephalic DA neurons, associated with a decrease in the density of DA innervation in the nucleus accumbens, reduced activity-dependent DA release, and impaired motor behavior. These findings provide strong evidence for a functional role of the glutamatergic cophenotype in the development of mesencephalic DA neurons, opening new perspectives into the pathophysiology of neurodegenerative disorders involving the mesostriatal DA system.

Hydraulic Fracturing in Western Siberia, Russia is a common practice that the need of stimulating wells in mature oil fields cannot be limited to non-complicated reservoirs. Wells requiring hydraulic fracturing with water zones either above or below is what we mean as reservoirs that require techniques that are out of the ordinary and day to day operations. A technique using dry solid material to contain the hydraulic fracture job within the zone of interest has been used in several wells and will be presented in this paper. The oil field Nivagalskoe which is located in Nizhnevartovsk area, Western Siberia, Russia is considered a mature oil field where due to the low value of the reservoir permeability, hydraulic fracturing these reservoirs require more than a skin bypass frac. Achieving a lengthier propped frac and descent net pressure increase is always a challenge on any hydraulic fracturing job execution but is always a must for the oil fields in the Nizhnevartovsk area. The challenge becomes when there are water zones close to the zone of interest to be stimulated via hydraulic fracturing. The objective is only not to design a job that will be limited in height by optimizing parameters commonly used in limiting frac geometry but to guarantee at best that the frac will not grow into the water zone. Several cas histories will be presented where all cases the water cut was measured before and after the hydraulic fracturing job. The technique used and recommendations will be presented to optimize future jobs. All other options to limit frac geometry considered will also be presented.

There are several active and passive methods for controlling the production of formation sand. The main objective is to produce at the maximum sand-free rate without compromising the well’s hydrocarbon production and longevity of the completion system placed. This paper presents a new chemical method and the latest case histories from offshore reservoirs. The new method changes the zeta potential of solid surfaces, thus strengthening attraction between the particles to optimally align proppant, sand particles, fines, or coal fines. Laboratory tests are presented, showing the mechanism of zeta potential and lab tests performed on unconsolidated cores by internal and independent labs. Core flood tests consisted of measuring the permeability before and after the treatment and the formation sand retention. More than 100 jobs have been treated with this chemical solution, with positive and promising results. Operational procedures and recommendations are presented on several wells treated.

Somatodendritic (STD) dopamine (DA) release is a key mechanism for the autoregulatory control of DA release in the brain. However, its molecular mechanism remains undetermined. We tested the hypothesis that differential expression of synaptotagmin (Syt) isoforms explains some of the differential properties of terminal and STD DA release. Down-regulation of the dendritically expressed Syt4 and Syt7 severely reduced STD DA release, whereas terminal release required Syt1. Moreover, we found that although mobilization of intracellular Ca(2+) stores is inefficient, Ca(2+) influx through N- and P/Q-type voltage-gated channels is critical to trigger STD DA release. Our findings provide an explanation for the differential Ca(2+) requirement of terminal and STD DA release. In addition, we propose that not all sources of intracellular Ca(2+) are equally efficient to trigger this release mechanism. Our findings have implications for a better understanding of a fundamental cell biological process mediating transcellular signaling in a system critical for diseases such as Parkinson disease.

SummaryThe natural response to itch sensation is to scratch, which relieves the itch through an unknown mechanism. Interaction between pain and itch has been frequently demonstrated, and the selectivity hypothesis of itch, based on data from electrophysiological and behavioral experiments, postulates the existence of primary pain afferents capable of repressing itch. Here, we demonstrate that deletion of vesicular glutamate transporter (VGLUT) 2 in a subpopulation of neurons partly overlapping with the vanilloid receptor (TRPV1) primary afferents resulted in a dramatic increase in itch behavior accompanied by a reduced responsiveness to thermal pain. The increased itch behavior was reduced by administration of antihistaminergic drugs and by genetic deletion of the gastrin-releasing peptide receptor, demonstrating a dependence on VGLUT2 to maintain normal levels of both histaminergic and nonhistaminergic itch. This study establishes that VGLUT2 is a major player in TRPV1 thermal nociception and also serves to regulate a normal itch response.

The “One neuron-one neurotransmitter” concept has been challenged frequently during the last three decades, and the coexistence of neurotransmitters in individual neurons is now regarded as a common phenomenon. The functional significance of neurotransmitter coexistence is, however, less well understood. Several studies have shown that a subpopulation of dopamine (DA) neurons in the ventral tegmental area (VTA) expresses the vesicular glutamate transporter 2 (VGLUT2) and has been suggested to use glutamate as a cotransmitter. The VTA dopamine neurons project to limbic structures including the nucleus accumbens, and are involved in mediating the motivational and locomotor activating effects of psychostimulants. To determine the functional role of glutamate cotransmission by these neurons, we deleted VGLUT2 in DA neurons by using a conditional gene-targeting approach in mice. A DAT-Cre/Vglut2Lox mouse line ( Vglut2 f/f;DAT-Cre mice) was produced and analyzed by in vivo amperometry as well as by several behavioral paradigms. Although basal motor function was normal in the Vglut2 f/f;DAT-Cre mice, their risk-taking behavior was altered. Interestingly, in both home-cage and novel environments, the gene targeted mice showed a greatly blunted locomotor response to the psychostimulant amphetamine, which acts via the midbrain DA system. Our results show that VGLUT2 expression in DA neurons is required for normal emotional reactivity as well as for psychostimulant-mediated behavioral activation.