In vivo Kinetic Biodistribution of Nano‐Sized Outer Membrane Vesicles Derived from Bacteria

Evaluation of kinetic distribution and behaviors of nanoparticles in vivo provides crucial clues into their roles in living organisms. Extracellular vesicles are evolutionary conserved nanoparticles, known to play important biological functions in intercellular, inter‐species, and inter‐kingdom communication. In this study, the first kinetic analysis of the biodistribution of outer membrane vesicles (OMVs)—bacterial extracellular vesicles—with immune‐modulatory functions is performed. OMVs, injected intraperitoneally, spread to the whole mouse body and accumulate in the liver, lung, spleen, and kidney within 3 h of administration. As an early systemic inflammation response, increased levels of TNF‐α and IL‐6 are observed in serum and bronchoalveolar lavage fluid. In addition, the number of leukocytes and platelets in the blood is decreased. OMVs and cytokine concentrations, as well as body temperature are gradually decreased 6 h after OMV injection, in concomitance with the formation of eye exudates, and of an increase in ICAM‐1 levels in the lung. Following OMV elimination, most of the inflammatory signs are reverted, 12 h post‐injection. However, leukocytes in bronchoalveolar lavage fluid are increased as a late reaction. Taken together, these results suggest that OMVs are effective mediators of long distance communication in vivo.     

A review on some classes of algebraic systems

ABSTRACT

In this paper, we review some algebraic control system. Precisely, linear and bilinear systems on Euclidean spaces and invariant and linear systems on Lie groups. The fourth classes of systems have a common issue: to any class, there exists an associated subgroup. From this object, we survey the controllability property. Especially, from those coming from our contribution to the theory.     

Infrequent association: massive inguinoscrotal bladder hernia, multiple scrotal lithiasis, and bilateral obstructive uropathy]

BACKGROUND AND AIMS OF THE STUDY: Surgical treatment of functional tricuspid regurgitation associated with advanced valvular disease remains controversial, mainly due to the difficulty in choosing between valve replacement or reconstructive surgery. Failure to correct tricuspid regurgitation during valvular surgery carries a poor prognosis, as reoperation may represent a significant risk to the patient. Thus correct tricuspid valve surgery is vital to achieve improved early and long-term clinical results. METHODS: A total of 142 patients underwent concomitant tricuspid annuloplasty with mitral valve replacement and their clinical outcome was assessed. RESULTS: Overall hospital mortality rate was 11.3%. Seven patients died during follow up. The overall actuarial survival rate for 10 years was 74.1 +/- 14.2%. Postoperatively, 75.8% of the patients had no residual tricuspid insufficiency, while 24.2% had first- or second-degree tricuspid insufficiency and were treated medically. All patients were in NHYA functional class I or II postoperatively. CONCLUSION: Clinically, it is important to assess the severity of functional tricuspid insufficiency before and/or during the operation. Bicuspidalization annuloplasty for functional tricuspid insufficiency has provided good results, with a low incidence of reoperation and significant clinical improvement. The technique could be used in most patients with functional tricuspid regurgitation.     

Social constructivism and capacity building for environmental governance

The concept of capacity building for environmental protection and management refers to the process by which the abilities of societies to identify and solve environmental problems are developed. This work assumes a social constructivist perspective to the process of capacity building for environmental governance and thus depicts it as critically dependent on broader processes of political and socioeconomic modernization. In an attempt to depict such a process, a heuristic device is put forward as a model of capacity analysis. A theoretical discourse and new institutionalist perspectives are used to test such a model on a theoretical plane. Some preliminary conclusions are drawn from this theoretical exercise.     

Long-term follow-up of immunosuppressive treatment for obstructive airways disease after allogeneic bone marrow transplantation

BACKGROUND: Locally advanced thyroid cancer invading the tracheal cartilage represents a difficult treatment dilemma during thyroidectomy. METHODS: A retrospective chart review was performed to determine the results of laryngotracheal resection or tracheal cartilage shave with adjuvant radiotherapy in patients with locally advanced thyroid cancer invading the upper airway. RESULTS: Of 597 patients undergoing thyroidectomy for thyroid cancer, 40 were found to have laryngotracheal invasion. Thirty-five patients with superficial invasion underwent cartilage shave procedures with adjuvant radiotherapy; five with full-thickness invasion underwent radical resection, including tracheal sleeve resection (n = 3) or total laryngectomy (n = 2). Histologic subtypes included papillary (n = 32), follicular (n = 2), Hurthle cell (n = 1), medullary (n = 3), and anaplastic (n = 2). Of the cartilage shave group, 25 are currently alive with no evidence of disease at a mean follow-up of 81 months (range 1-290). Six developed isolated local/regional recurrence and were managed with total laryngectomy (n = 1), tracheal resection (n = 1), cervical lymphadenectomy (n = 1), or repeat radiotherapy (n = 3). All six patients remain free of disease at a mean follow-up of 5 years. Of those who underwent initial laryngotracheal resection, four remain free of disease at a mean follow-up of 5 years. The rates of 10-year disease-free survival and overall survival for all patients were 47.9% (95% confidence interval [CI] 24.8, 71.0) and 83.9% (95% CI 70.3, 97.5), respectively. CONCLUSIONS: These data suggest that adequate management of thyroid cancer with laryngotracheal invasion can be achieved with a more conservative surgical approach and adjuvant radiotherapy, reserving more radical resections for extensive primary lesions or locally recurrent disease.     

Probabilistic linear models for multiresolution estimation in gray-level images

A multiresolution analysis of digital gray-level images is presented. A gray-level multi-scale framework is determined from two main assumptions: the gray scale is binary at the finest spatial resolution, and the gray levels of composed regions are obtained additively. In order to interrelate the gray-level histograms of the same image at different resolutions, probabilistic linear models are developed, which are then applied for estimation. Linear-optimization theory is used as a way of constructing such models. A general procedure for image processing is sketched, based on gray-level estimation. A versatile algorithm for nonlinear filtering is derived. Some examples of prospective applications are given.This work was partially supported by grant TIC91-646 from the DGYCIT of the Spanish Government.     

Deleting files in the Celeste peer-to-peer storage system

Celeste is a robust peer-to-peer object store built on top of a distributed hash table (DHT). Celeste is a working system, developed by Sun Microsystems Laboratories. During the development of Celeste, we faced the challenge of complete object deletion, and moreover, of deleting “files” composed of several different objects. This important problem is not solved by merely deleting meta-data, as there are scenarios in which all file contents must be deleted, e.g., due to a court order. Complete file deletion in a realistic peer-to-peer storage system has not been previously dealt with due to the intricacy of the problem — the system may experience high churn rates, nodes may crash or have intermittent connectivity, and the overlay network may become partitioned at times. We present an algorithm that eventually deletes all file contents, data and meta-data, in the aforementioned complex scenarios. The algorithm is fully functional and has been successfully integrated into Celeste.     

Distributed data clustering in sensor networks

Low overhead analysis of large distributed data sets is necessary for current data centers and for future sensor networks. In such systems, each node holds some data value, e.g., a local sensor read, and a concise picture of the global system state needs to be obtained. In resource-constrained environments like sensor networks, this needs to be done without collecting all the data at any location, i.e., in a distributed manner. To this end, we address the distributed clustering problem, in which numerous interconnected nodes compute a clustering of their data, i.e., partition these values into multiple clusters, and describe each cluster concisely. We present a generic algorithm that solves the distributed clustering problem and may be implemented in various topologies, using different clustering types. For example, the generic algorithm can be instantiated to cluster values according to distance, targeting the same problem as the famous k-means clustering algorithm. However, the distance criterion is often not sufficient to provide good clustering results. We present an instantiation of the generic algorithm that describes the values as a Gaussian Mixture (a set of weighted normal distributions), and uses machine learning tools for clustering decisions. Simulations show the robustness, speed and scalability of this algorithm. We prove that any implementation of the generic algorithm converges over any connected topology, clustering criterion and cluster representation, in fully asynchronous settings.     

An Analysis of Edge Detection by Using the Jensen-Shannon Divergence

This work constitutes a theoretical study of the edge-detection method by means of the Jensen-Shannon divergence, as proposed by the authors. The overall aim is to establish formally the suitability of the procedure of edge detection in digital images, as a step prior to segmentation. In specific, an analysis is made not only of the properties of the divergence used, but also of the method's sensitivity to the spatial variation, as well as the detection-error risk associated with the operating conditions due to the randomness of the spatial configuration of the pixels. Although the paper deals with the procedure based on the Jensen-Shannon divergence, some problems are also related to other methods based on local detection with a sliding window, and part of the study is focused to noisy and textured images.