An integrated modelling framework for neural circuits with multiple neuromodulators

Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to largescale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies

The second “time-out”: A surgical safety checklist for lengthy robotic surgeries

Robotic surgeries of long duration are associated with both increased risks to patients as well as distinct challenges for care providers. We propose a surgical checklist, to be completed during a second “time-out”, aimed at reducing peri-operative complications and addressing obstacles presented by lengthy robotic surgeries. A review of the literature was performed to identify the most common complications of robotic surgeries with extended operative times. A surgical checklist was developed with the goal of addressing these issues and maximizing patient safety. Extended operative times during robotic surgery increase patient risk for position-related complications and other adverse events. These cases also raise concerns for surgical, anesthesia, and nursing staff which are less common in shorter, non-robotic operations. Key elements of the checklist were designed to coordinate operative staff in verifying patient safety while addressing the unique concerns within each specialty. As robotic surgery is increasingly utilized, operations with long surgical times may become more common due to increased case complexity and surgeons overcoming the learning curve. A standardized surgical checklist, conducted three to four hours after the start of surgery, may enhance perioperative patient safety and quality of care

Experimental Validation of Bio-Inspired Control: 4 Story Shear Structure

With the rise of rapid urbanization, skyscrapers are increasingly common, making them vulnerable to environmental pressures such as high winds and earthquakes. These forces threaten the structural stability of tall buildings, necessitating the development of active control methods to mitigate their effects and maintain structural integrity. Traditional control systems utilize numerous sensing nodes that feed data into a centralized system, which then determines the appropriate actions for the actuators. However, this centralized approach can introduce substantial lag due to the overwhelming amount of data being processed. By transitioning to a decentralized wireless system, where sensors directly feed data to control nodes, scalability is improved, and lag is reduced. However, the wireless nature increases the risk of data loss. The proposed solution draws inspiration from the biological central nervous system, with sensing nodes performing upfront signal processing. A weighting matrix determines how data is transmitted to motor neurons, which decide actuator firing and force application. Using the Linear Quadratic Regulator (LQR) control theory, weights are optimized by modeling structural behavior while striving to minimize floor displacement and acceleration. The experimental setup involved a four-story shear structure outfitted with position sensors and accelerometers, which fed data into sensor nodes. These nodes relayed information to control nodes that calculated the necessary control force, which was then transmitted to actuator carts, ensuring effective stabilization of the structure

Performance Appraisal of Dragline Mining in India

Draglines have been abundantly used in coal mining for decades, either as stripper or stripper and coal extractor. As this equipment possesses certain inherent advantages, which their rivals do not, they must be operated in a round-the-clock fashion for high productivity and low costs. In India, the development of giant surface mining ventures like Bina and Jayant with setting up of higher coal production targets (upto 10 million tonnes per annum) calls for systems to remove large volume of overburden in shortest possible time. This has resulted in major changes in overburden/interburden excavation technology in surface coal mines from shovel mining to that of draglines. Coal India Limited (CIL) has now standardized the draglines in two sizes, which are 10/70 and 24/96 for their mines. Most mines depend on the dragline 24 hours a day, 7 days a week. In many coal mines, it is the only primary stripping tool and the mine's output is totally dependent on the dragline’s performance. For these reasons, dragline design requires emphasis placed on developing component’s with high levels of reliability and predictability so that repairs and replacement of components can be scheduled at times that will least affect the overall mining operation. Prior to deploying draglines in mines, various factors have to be considered for selection of suitable size. Different parameters are used to determine the production and productivity of draglines. In this thesis these points are discussed in detail

S4 Movement in a Mammalian HCN Channel

Hyperpolarization-activated, cyclic nucleotide–gated ion channels (HCN) mediate an inward cation current that contributes to spontaneous rhythmic firing activity in the heart and the brain. HCN channels share sequence homology with depolarization-activated Kv channels, including six transmembrane domains and a positively charged S4 segment. S4 has been shown to function as the voltage sensor and to undergo a voltage-dependent movement in the Shaker K+ channel (a Kv channel) and in the spHCN channel (an HCN channel from sea urchin). However, it is still unknown whether S4 undergoes a similar movement in mammalian HCN channels. In this study, we used cysteine accessibility to determine whether there is voltage-dependent S4 movement in a mammalian HCN1 channel. Six cysteine mutations (R247C, T249C, I251C, S253C, L254C, and S261C) were used to assess S4 movement of the heterologously expressed HCN1 channel in Xenopus oocytes. We found a state-dependent accessibility for four S4 residues: T249C and S253C from the extracellular solution, and L254C and S261C from the internal solution. We conclude that S4 moves in a voltage-dependent manner in HCN1 channels, similar to its movement in the spHCN channel. This S4 movement suggests that the role of S4 as a voltage sensor is conserved in HCN channels. In addition, to determine the reason for the different cAMP modulation and the different voltage range of activation in spHCN channels compared with HCN1 channels, we constructed a COOH-terminal–deleted spHCN. This channel appeared to be similar to a COOH-terminal–deleted HCN1 channel, suggesting that the main functional differences between spHCN and HCN1 channels are due to differences in their COOH termini or in the interaction between the COOH terminus and the rest of the channel protein in spHCN channels compared with HCN1 channels

Assessment Of Sensor Based Precision Nitrogen Management For Enhancing Productivity And Profitability Of Maize In Godavari Delta Of Andhra Pradesh

A field study was conducted at Agricultural Research Station, Peddapuram, Kakinada Dist, Andhra Pradesh, India during kharif 2018 and 2019 on a sandy loam soils to assess effect of sensor-based nitrogen application on growth, yield and economics of maize.  The precision nutrient management practices had significant effect on the growth and yield attributes of maize. It was observed that in second year, higher yield level obtained in all treatment and corresponding increase in the N dose under GS guided N application was also observed which shows that this tool optimizes the N application as per yield target/potential. The green seeker (GS) based precision nutrient management practice increased grain yield of maize to the tune of 3.4-5.6 per cent over recommended doses of fertilizers( RDF).The adoption of GS guided nitrogen application increased the net returns by Rs. 4,257-6,273 ha-1 over RDF by saving money on costly fertilizer inputs. These GS based treatments gave 7.1to 10.5 % higher net returns along with 29% to 49% increased agronomic N use efficiency and saving of 23.5 to 61.5 Kg N /ha over blanket RDF of 200 kg N/ha. Our experimental results disclosed that in-season N management based on green seeker sensor-based technology could be a better strategy towards higher yield with optimized N use efficiency and thereby reducing the cost of cultivation than blanket recommendations in maize

Ultrafine aluminium: Quench collection of agglomerates

Combustion of aluminized solid propellants exhibits phenomena associated with accumulation, agglomeration, ignition, and combustion of ultra-fine aluminium particles. In this study, agglomeration phenomenon of ultra-fine aluminium in solid propellant combustion is investigated using quench collection experimental technique over the pressure ranges from 2MPa to 8MPa. The ultra-fine aluminium powder synthesized by Radio Frequency Induction Plasma technique having harmonic mean size of 438nm is used for agglomeration study. The quenching distance is varied from 5mm to 71mm from the propellant burning surface to estimate the effect on agglomerate size. The morphology and chemical compositions of the collected products were then studied by using scanning electron microscopy coupled with energy dispersive (SEM-EDS) method. Under the explored experimental conditions, the results confirm that ultra-fine aluminium propellant show aggregation/agglomeration with the size ranging from 11 – 21 μm in combustion products. Smaller diameter condensed phase products will likely decrease two-phase flow loss and reduce slag accumulation

Application of artificial neural networks for the prediction of aluminium agglomeration processes

Aluminium is universal and vital constituent in composite propellants and typically used to improve performance. Aluminum agglomeration takes place on the burning surface of aluminized propellants, which leads to reduced combustion efficiency and 2P flow losses. To understand the processes and behaviour of aluminum agglomeration, particles size distribution of composite propellants were studied using a quench particle collection technique, at 2 to 8 MPa and varying quench distances from 5mm to 71mm. To predict the agglomerate diameter of metallized/ultra-fine aluminium of composite propellants, a new artificial neural network (ANN) model was derived. Five Layered Feed Forward Back Propagation Neural Network was developed with sigmoid as a transfer function by varying feed variables in input layer such as Quench distance (QD) and pressure (P). The ANN design was trained victimization stopping criterion as one thousand iterations. Five ANN models dealing with the combustion of AP/Al/HTPB and one ANN model of AP/UFAl/HTPB composite propellants were presented. The validated ANN models will be able to predict unexplored regimes wherein experimental data are not available. From the present work it was ascertained that, for agglomeration produced by quench collection technique, the ANN is one of a substitute method to predict the agglomerate diameter and results can be evaluated rather than experimented, with reduced time and cost. The resulting agglomerates sizes from ANN model, matches with the experimental results. The percentage error is less than 3.0% of the label propellants used in this work