Electromagnetic interventions as a therapeutic approach to spreading depression

Spreading depression (SD) is a slow propagating wave of depolarization that can spread throughout the cortex in the event of brain injury or any general energy failure of the brain. Massive cellular depolarization causes enormous ionic and water shifts and silences synaptic transmission in the affected tissue. Large amounts of energy are required to restore ionic gradients and are not always met. When these energetic demands are not met, brain tissue damage can occur. The exact mechanism behind initiation and propagation of SD are unknown, but a general model is known. It may be possible to prevent or delay the onset of SD using non-invasive electromagnetic techniques. Transcranial magnetic stimulation (TMS), electrical stimulation (ES), and transcranial direct coupled stimulation (tDCS) could be used to decrease neuronal excitability in different ways. In theory, any technique that can reduce cortical excitability could suppress SD initiating or propagating

Improving Performance and Flexibility of Fabric-Attached Memory Systems

As demands for memory-intensive applications continue to grow, the memory capacity of each computing node is expected to grow at a similar pace. In high-performance computing (HPC) systems, the memory capacity per compute node is decided upon the most demanding application that would likely run on such a system, and hence the average capacity per node in future HPC systems is expected to grow significantly. However, diverse applications run on HPC systems with different memory requirements and memory utilization can fluctuate widely from one application to another. Since memory modules are private for a corresponding computing node, a large percentage of the overall memory capacity will likely be underutilized, especially when there are many jobs with small memory footprints. Thus, as HPC systems are moving towards the exascale era, better utilization of memory is strongly desired. Moreover, as new memory technologies come on the market, the flexibility of upgrading memory and system updates becomes a major concern since memory modules are tightly coupled with the computing nodes. To address these issues, vendors are exploring fabric-attached memories (FAM) systems. In this type of system, resources are decoupled and are maintained independently. Such a design has driven technology providers to develop new protocols, such as cache-coherent interconnects and memory semantic fabrics, to connect various discrete resources and help users leverage advances in-memory technologies to satisfy growing memory and storage demands. Using these new protocols, FAM can be directly attached to a system interconnect and be easily integrated with a variety of processing elements (PEs). Moreover, systems that support FAM can be smoothly upgraded and allow multiple PEs to share the FAM memory pools using well-defined protocols. The sharing of FAM between PEs allows efficient data sharing, improves memory utilization, reduces cost by allowing flexible integration of different PEs and memory modules from several vendors, and makes it easier to upgrade the system. However, adopting FAM in HPC systems brings in new challenges. Since memory is disaggregated and is accessed through fabric networks, latency in accessing memory (efficiency) is a crucial concern. In addition, quality of service, security from neighbor nodes, coherency, and address translation overhead to access FAM are some of the problems that require rethinking for FAM systems. To this end, we study and discuss various challenges that need to be addressed in FAM systems. Firstly, we developed a simulating environment to mimic and analyze FAM systems. Further, we showcase our work in addressing the challenges to improve the performance and increase the feasibility of such systems; enforcing quality of service, providing page migration support, and enhancing security from malicious neighbor nodes

Reliability of Nine Programs of Topological Predictions and Their Application to Integral Membrane Channel and Carrier Proteins

We evaluated topological predictions for nine different programs, HMMTOP, TMHMM, SVMTOP, DAS, SOSUI, TOPCONS, PHOBIUS, MEMSAT-SVM (hereinafter referred to as MEMSAT), and SPOCTOPUS. These programs were first evaluated using four large topologically well-defined families of secondary transporters, and the three best programs were further evaluated using topologically more diverse families of channels and carriers. In the initial studies, the order of accuracy was: SPOCTOPUS > MEMSAT > HMMTOP > TOPCONS > PHOBIUS > TMHMM > SVMTOP > DAS > SOSUI. Some families, such as the Sugar Porter Family (2.A.1.1) of the Major Facilitator Superfamily (MFS; TC #2.A.1) and the Amino Acid/Polyamine/Organocation (APC) Family (TC #2.A.3), were correctly predicted with high accuracy while others, such as the Mitochondrial Carrier (MC) (TC #2.A.29) and the K(+) transporter (Trk) families (TC #2.A.38), were predicted with much lower accuracy. For small, topologically homogeneous families, SPOCTOPUS and MEMSAT were generally most reliable, while with large, more diverse superfamilies, HMMTOP often proved to have the greatest prediction accuracy. We next developed a novel program, TM-STATS, that tabulates HMMTOP, SPOCTOPUS or MEMSAT-based topological predictions for any subdivision (class, subclass, superfamily, family, subfamily, or any combination of these) of the Transporter Classification Database (TCDB; www.tcdb.org) and examined the following subclasses: α-type channel proteins (TC subclasses 1.A and 1.E), secreted pore-forming toxins (TC subclass 1.C) and secondary carriers (subclass 2.A). Histograms were generated for each of these subclasses, and the results were analyzed according to subclass, family and protein. The results provide an update of topological predictions for integral membrane transport proteins as well as guides for the development of more reliable topological prediction programs, taking family-specific characteristics into account

Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization

Ship routing and scheduling problem is considered to meet the demand for various products in multiple ports within the planning horizon. The ports have restricted operating time, so multiple time windows are taken into account. The problem addresses the operational measures such as speed optimisation and slow steaming for reducing carbon emission. A Mixed Integer Non-Linear Programming (MINLP) model is presented and it includes the issues pertaining to multiple time horizons, sustainability aspects and varying demand and supply at various ports. The formulation incorporates several real time constraints addressing the multiple time window, varying supply and demand, carbon emission, etc. that conceive a way to represent several complicating scenarios experienced in maritime transportation. Owing to the inherent complexity, such a problem is considered to be NP-Hard in nature and for solutions an effective meta-heuristics named Particle Swarm Optimization-Composite Particle (PSO-CP) is employed. Results obtained from PSO-CP are compared using PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to prove its superiority. Addition of sustainability constraints leads to a 4–10% variation in the total cost. Results suggest that the carbon emission, fuel cost and fuel consumption constraints can be comfortably added to the mathematical model for encapsulating the sustainability dimensions

Design of Multi-Layer Protocol Architecture using Hybrid Optimal Link State Routing (HOLSR) Protocol for CR Networks

There is a lack of spectrum due to the rising demand for sensing device communication and the inefficient use of the existing available spectrum. Through opportunistic access to licenced bands, which does not obstruct the primary sensory users (PU), it is feasible to enhance the inefficient use of the current sensor device frequency spectrum. Cognitive settings are a demanding environment in which to carry out tasks like sensor network routing and spectrum access since it is difficult to access channels due to the presence of PUs. The basic goal of the routing problem in sensor networks is to establish and maintain wireless sensor multihop paths between cognitive sensor nodes. The frequency to be used as well as the number of hops at each sensor node along the path must be determined for this assignment. In order to improve performance while using less energy, scientists suggested a unique adaptive cross-layer optimisation subcarrier distribution technique with the HOLSR protocol for wireless sensor nodes. Throughput and energy consumption parameters are used to analyse the sensor network architecture protocol that has been developed. The energy usage of the sensor nodes in the network has increased by 50%. The performance of the proposed HOLSR algorithm is assessed using the simulation results, and the results are contrasted with those of a conventional multicarrier (MC) system in terms of bit error rate and throughput

5G Enabled Moving Robot Captured Image Encryption with Principal Component Analysis Method

Estimating the captured image of moving robots is very difficult. These images are vital in analyzing earth's surface objects for many applications like studying environmental conditions, Land use and Land Cover changes, and change detection studies of worldwide change. Multispectral robot-captured images have a massive amount of low-resolution data, which is lost due to a lack of capture efficiency due to artificial and atmospheric reasons. The image transformation is required in a 5G network with effective transmission by reducing noise, inconsistent lighting, and low resolution, degrading image quality. In this paper, the authors proposed the machine learning dimensionality reduction technique i.e. Principle Component Analysis (PCA) and which is used for metastasizing the 5 G-enabled moving robot captured image to enrich the image's visual perception to analyze the exact information of global or local data. The encryption algorithm implanted for data reduction and transmission over the 5G network gives sophisticated results compared with other standard methods. This proposed algorithm gives better performance in developing data reduction, network convergence speed, reduces the training time for object classification, and improves accuracy for multispectral moving robot-captured images by the support of 5G network

Characterization of the Tetraspan Junctional Complex (4JC) superfamily

Connexins or innexins form gap junctions, while claudins and occludins form tight junctions. In this study, statistical data, derived using novel software, indicate that these four junctional protein families and eleven other families of channel and channel auxiliary proteins are related by common descent and comprise the Tetraspan (4 TMS) Junctional Complex (4JC) Superfamily. These proteins all share similar 4 transmembrane α-helical (TMS) topologies. Evidence is presented that they arose via an intragenic duplication event, whereby a 2 TMS-encoding genetic element duplicated tandemly to give 4 TMS proteins. In cases where high resolution structural data were available, the conclusion of homology was supported by conducting structural comparisons. Phylogenetic trees reveal the probable relationships of these 15 families to each other. Long homologues containing fusions to other recognizable domains as well as internally duplicated or fused domains are reported. Large “fusion” proteins containing 4JC domains proved to fall predominantly into family-specific patterns as follows: (1) the 4JC domain was N-terminal; (2) the 4JC domain was C-terminal; (3) the 4JC domain was duplicated or occasionally triplicated and (4) mixed fusion types were present. Our observations provide insight into the evolutionary origins and subfunctions of these proteins as well as guides concerning their structural and functional relationships

A Case of Glycogenic Hepatopathy as a Complication of Poorly Controlled Type 1 Diabetes Mellitus

A 23-year-old African American male with a medical history significant for poorly controlled type 1 diabetes mellitus (T1DM) presented with abdominal pain and vomiting. His laboratory workup was consistent with diabetic ketoacidosis (DKA). An acute elevation of liver enzymes was noted as the DKA resolved, with the alanine transferase and aspartate transferase levels elevated to more than 50 times the normal limit within the next 24 hours. Because abnormal liver function tests are found frequently in patients with type 1 diabetes mellitus, it is important to have a broad differential diagnosis. Furthermore, a low threshold of suspicion is required to identify a relatively underdiagnosed etiology like glycogenic hepatopathy (GH). This case report describes how patterns and trends of liver function tests provide important clues to the diagnosis of GH; how imaging modalities like ultrasonography, computerized tomography (CT) scan, and magnetic resonance imaging (MRI) scan could be used to differentiate GH from nonalcoholic fatty liver disease (NAFLD); and how the diagnosis of GH can be made without the need for invasive liver biopsy. The knowledge about GH should prevent its delayed diagnosis and improve the outcomes by appropriately managing uncontrolled type 1 DM

Reliability of nine programs of topological predictions and their application to integral membrane channel and carrier proteins.

We evaluated topological predictions for nine different programs, HMMTOP, TMHMM, SVMTOP, DAS, SOSUI, TOPCONS, PHOBIUS, MEMSAT-SVM (hereinafter referred to as MEMSAT), and SPOCTOPUS. These programs were first evaluated using four large topologically well-defined families of secondary transporters, and the three best programs were further evaluated using topologically more diverse families of channels and carriers. In the initial studies, the order of accuracy was: SPOCTOPUS > MEMSAT > HMMTOP > TOPCONS > PHOBIUS > TMHMM > SVMTOP > DAS > SOSUI. Some families, such as the Sugar Porter Family (2.A.1.1) of the Major Facilitator Superfamily (MFS; TC #2.A.1) and the Amino Acid/Polyamine/Organocation (APC) Family (TC #2.A.3), were correctly predicted with high accuracy while others, such as the Mitochondrial Carrier (MC) (TC #2.A.29) and the K(+) transporter (Trk) families (TC #2.A.38), were predicted with much lower accuracy. For small, topologically homogeneous families, SPOCTOPUS and MEMSAT were generally most reliable, while with large, more diverse superfamilies, HMMTOP often proved to have the greatest prediction accuracy. We next developed a novel program, TM-STATS, that tabulates HMMTOP, SPOCTOPUS or MEMSAT-based topological predictions for any subdivision (class, subclass, superfamily, family, subfamily, or any combination of these) of the Transporter Classification Database (TCDB; www.tcdb.org) and examined the following subclasses: α-type channel proteins (TC subclasses 1.A and 1.E), secreted pore-forming toxins (TC subclass 1.C) and secondary carriers (subclass 2.A). Histograms were generated for each of these subclasses, and the results were analyzed according to subclass, family and protein. The results provide an update of topological predictions for integral membrane transport proteins as well as guides for the development of more reliable topological prediction programs, taking family-specific characteristics into account