Symptomatic giant left atrial aneurysm in a child : a rare entity

Isolated left atrial aneurysms are rare entities in clinical practice. Usually the condition is diagnosed in the second to fourth decades of life. The presence of such lesions in the pediatric age group is scantily described. We present a 2 year boy who presented with complaints of excessive irritability, respiratory distress and swelling of the feet. On examination, child was tachypnoeic with irregularly irregular rhythm. Echo showed a huge aneurysmal LA appendage with severe left ventricle dysfunction. The child underwent surgical resection for same. Findings were confirmed intraoperatively but he continued to have low cardiac output state after the surgery, with frequent arrhythmias and expired on day 7 of surgery. The case is reviewed and compared with the available English literature.peer-reviewe

Process control & instrumentation for titanium sponge plant

A large scale facility for establishing the technology of production of titanium sponge in 4000 kg batches by high temperature reduction of titanium tetrachloride has been set up at the Defence Metallurgical Research Laboratory, Hyderabad. The steps involved in the process are (i) two stage distillation for the purification of titanium tetrachloride (ii) high temperature reduction of titanium tetrachloride by molten magnesium followed by pyro-vacuum distillation of sponge in a combined reductionvacuum disti-llation furnace and (iii) processing of the sponge produ-ced which include sponge ejection, cutting, crushing and blending. To produce high purity sponge in a reproducible manner batch after batch, very close monitoring & control of process parameters is essential. In view of the highly reactive nature of the metal, any post reduction purifica-tion is totally ruled out. This paper discusses in detail the process control scheme adopted for the titanium tetra-chloride purification and reduction operations, highlight-ing the type of instrumentation adopted considering the corrosive nature of titanium tetrachloride. A microproce-ssor based Distributed Control System has been installed for controlling the process operation. Salient features of this system are also described in this paper

Glial-derived neurotrophic factor modulates enteric neuronal survival and proliferation through neuropeptide Y.

BACKGROUND & AIMS: Glial-derived neurotrophic factor (GDNF) promotes the survival and proliferation of enteric neurons. Neuropeptide Y (NPY) is an important peptide regulating gastrointestinal motility. The role of NPY on the survival and proliferation of enteric neurons is not known. We examined the effects of GDNF on the expression and release of NPY from enteric neurons and the role of NPY in promoting enteric neuronal proliferation and survival. METHODS: Studies were performed in primary enteric neuronal cultures and NPY knockout mice (NPY(-/-)). GDNF-induced expression of NPY was assessed by reverse-transcription polymerase chain reaction (RT-PCR), immunocytochemistry, and enzyme-linked immunosorbent assay. Using NPY-siRNA and NPY-Y1 receptor antagonist, we examined the role of NPY in mediating the survival and proliferation effects of GDNF. Gastrointestinal motility was assessed by measuring gastric emptying, intestinal transit, and isometric muscle recording from intestinal muscle strips. RESULTS: GDNF induced a significant increase in NPY messenger RNA and protein expression in primary enteric neurons and the release of NPY into the culture medium. NPY (1 mumol/L) significantly increased proliferation of neurons and reduced apoptosis. In the presence of NPY-siRNA and NPY-Y1 receptor antagonist or in enteric neurons cultured from NPY(-/-) mice, GDNF-mediated neuronal proliferation and survival was reduced. NPY increased the phosphorylation of Akt, a downstream target of the PI-3-kinase pathway. In NPY(-/-) mice, there were significantly fewer nNOS-containing enteric neurons compared with wild-type (WT) mice. NPY(-/-) mice had accelerated gastric emptying and delayed intestinal transit compared with WT mice. CONCLUSIONS: We demonstrate that NPY acts as an autocrine neurotrophic factor for enteric neurons

Spinal cord atrophy and myelomalacia following triple intrathecal chemotherapy in a patient of relapsed acute lymphoblastic leukemia

Paraplegia in a case of leukemia is an uncommon finding. It can be disease related, therapy related, or both. It may or maynot be reversible or curable. Here, we are discussing an unusual acute life-threatening, therapy related condition, where tripleintrathecal therapy in a relapsed acute lymphoblastic leukemia child led to severe spinal cord atrophy and myelomalacia causingacute paraplegia with urinary retention. Subsequently, the patient developed respiratory failure and succumbed to death. There isvery few case reported of this complication. The aim of this case report is to sensitize, the pediatricians and pediatric oncologistsabout this life-threatening complication of chemotherapy

Online Linear Optimization with Inventory Management Constraints

This paper considers the problem of online linear optimization with inventory management constraints. Specifically, we consider an online scenario where a decision maker needs to satisfy her timevarying demand for some units of an asset, either from a market with a time-varying price or from her own inventory. In each time slot, the decision maker is presented a (linear) price and must immediately decide the amount to purchase for covering the demand and/or for storing in the inventory for future use. The inventory has a limited capacity and can be used to buy and store assets at low price and cover the demand when the price is high. The ultimate goal of the decision maker is to cover the demand at each time slot while minimizing the cost of buying assets from the market. We propose ARP, an online algorithm for linear programming with inventory constraints, and ARPRate, an extended version that handles rate constraints to/from the inventory. Both ARP and ARPRate achieve optimal competitive ratios, meaning that no other online algorithm can achieve a better theoretical guarantee. To illustrate the results, we use the proposed algorithms in a case study focused on energy procurement and storage management strategies for data centers

GRADES: Gradient descent for similarity caching

International audienceA similarity cache can reply to a query for an object with similar objects stored locally. In some applications of similarity caches, queries and objects are naturally represented as points in a continuous space. Examples include 360° videos where user's head orientation-expressed in spherical coordinates determines what part of the video needs to be retrieved, and recommendation systems where the objects are embedded in a finite-dimensional space with a distance metric to capture content dissimilarity. Existing similarity caching policies are simple modifications of classic policies like LRU, LFU, and qLRU and ignore the continuous nature of the space where objects are embedded. In this paper, we propose GRADES, a new similarity caching policy that uses gradient descent to navigate the continuous space and find the optimal objects to store in the cache. We provide theoretical convergence guarantees and show GRADES increases the similarity of the objects served by the cache in both applications mentioned above

GRADES: Gradient descent for similarity caching

A similarity cache can reply to a query for an object with similar objects stored locally. In some applications of similarity caches, queries and objects are naturally represented as points in a continuous space. Examples include 360° videos where user's head orientation - expressed in spherical coordinates - determines what part of the video needs to be retrieved, and recommendation systems where the objects are embedded in a finite-dimensional space with a distance metric to capture content dissimilarity. Existing similarity caching policies are simple modifications of classic policies like LRU, LFU, and qLRU and ignore the continuous nature of the space where objects are embedded. In this paper, we propose Grades, a new similarity caching policy that uses gradient descent to navigate the continuous space and find the optimal objects to store in the cache. We provide theoretical convergence guarantees and show Grades increases the similarity of the objects served by the cache in both applications mentioned above