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Applications of the Internet of Things (IoT) are famously known for connecting devices via the internet. The main purpose of IoT systems (wireless or wired) is to connect devices together for data collection, buffering and data gateway. The collected large size of data is often captured from remote sources for automatic data analytics or for direct decision making by its users. This paper applies the programming pattern for Big Data in IoT systems that makes use of lightweight Java methods, introduced in the recently published work on ClientNet Distributed Cluster. Considering Big Data in IoT systems means the sensing of data from different resources, the network of IoT devices collaborating in data collection and processing; and the gateways servers where the resulting big data is supposed to be directed or further processed. This mainly involves resolving the issues of Big Data, i.e., the size and the network transfer speed along with many other issues of coordination and concurrency. The computer network that connects IoT may further include techniques such as Fog and Edge computing that resolve much of the network issues. This paper provides solutions to these problems that occur in wireless and wired systems. The talk is about the ClientNet programming model and its application in IoT systems for orchestration, such as coordination, data communication, device identification and synchronization between the gateway servers and devices. These devices include sensors attached with appliances (e.g., home automations, supply chain systems, light and heavy machines, vehicles, power grids etc.) or buildings, bridges and computers running data processing applications. As described in earlier papers, the introduced ClientNet techniques prevent from big data transfers and streaming that occupy more resources (hardware and bandwidth) and time. The idea is motivated by Big Data problems that make it difficult to collect it from different resources through small devices and then redirecting it. The proposed programming model of ClientNet Distributed Cluster stores Big Data on the nearest server coordinated by the nearest coordinator. The gateways and the systems that run analytics programs communicate by running programs from other computers when it is essentially required. This makes it possible to let Big Data rarely move across a communication network and allow only the source code to move around the network. The given programming model greatly simplifies data communication overheads, communication patterns among devices, networks and servers.

Background: To evaluate the surgical and medical efficacy of the patients operated for Ventricular Septal Defect (VSD) with Pulmonary Hypertension and Pulmonary Vascular Resistance (PVR). Infants and children with elevated PVR and Pulmonary Hypertension are associated with significant mortality and morbidity after surgical closure. Circulatory assist devices and sophisticated medicines may not be available to help in the management of infants and children with elevated Pulmonary artery pressure and resistance. We left Patent Foramen Ovale (PFO) or made atrial communication to decrease the morbidity and mortality associated with the closure of large VSD in this risky group., Methods: Sixteen infants and children were operated with median age of 12 months, operated by the same surgeon (IU), from January' 2004 to December' 2005. They were with large VSD of elevated PVR (3.9+0.3) and underwent VSD closure leaving PFO or artificial ASD (5mm). Surgical approach was through right atrium. Post operatively, all the patients were electively ventilated for 36 hours. They were given intravenous dilators (Glyceral Trinitrate + Phentolamine) and oral Sildenafil up to 1 mg /Kg, six hourly. Five cases went into acute pulmonary hypertensive crisis postoperatively, and were rescued by Prostacycline Nebulization., Results: Sixteen patients had VSD as the primary lesion that underwent operation. The overall early mortality was 6.25% (1/16). There have been no late deaths., Conclusion: Closure of large VSD with elevated PVR can be performed, leaving PFO or artificial ASD, with acceptable mortality and morbidity.

The neuropeptides galanin and pituitary adenylate cyclase-activating peptide (PACAP) have been implicated in the physiological regulation of lactotroph function. Using the 235-1 clonal lactotroph rat cell line we have studied the signalling pathways mediating the secretory and mitogenic responses to galanin and PACAP. Both peptides stimulated prolactin release to a similar maximal extent. PACAP (100 nM) stimulated an increase in the proliferation rate of 235-1 cells, but was significantly less effective than 100 nM galanin (161.8 +/- 2.3% vs 296.1 +/- 9.1% of control). PACAP stimulated cAMP accumulation with an ED50 of 3.2 nM, and a maximal effect of almost two-fold at a concentration of 100 nM. Galanin depleted cAMP, by 30% at a concentration of 100 nM. The aminosteroid phospholipase C (PLC) inhibitor U-73122 virtually abolished maximal peptide stimulated prolactin release. Depletion of inositol phosphates or downregulation of protein kinase C reduced maximal peptide stimulated prolactin release from about 260% to about 160% of unstimulated release. Both peptides at a concentration of 100 nM caused a sustained increase in intracellular calcium when incubated with cells for 30 min. These results demonstrate that both peptides stimulate prolactin release and the proliferation rate of 235-1 cells. The most important signalling pathway for prolactin release activated by both peptides is via PLC, although they also regulate cAMP levels, which are increased by PACAP and decreased by galanin. Despite maximal peptide stimulated prolactin release being equal, galanin has a greater mitogenic effect on 235-1 cells than PACAP, raising the possibility that it activates an additional mitogenic signalling pathway.