Towards Real-time Remote Processing of Laparoscopic Video

Laparoscopic surgery is a minimally invasive technique where surgeons insert a small video camera into the patient\u27s body to visualize internal organs and use small tools to perform these procedures. However, the benefit of small incisions has a disadvantage of limited visualization of subsurface tissues. Image-guided surgery (IGS) uses pre-operative and intra-operative images to map subsurface structures and can reduce the limitations of laparoscopic surgery. One particular laparoscopic system is the daVinci-si robotic surgical vision system. The video streams generate approximately 360 megabytes of data per second, demonstrating a trend toward increased data sizes in medicine, primarily due to higher-resolution video cameras and imaging equipment. Real-time processing this large stream of data on a bedside PC, single or dual node setup, may be challenging and a high-performance computing (HPC) environment is not typically available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second rate (fps), it is required that each 11.9 MB (1080p) video frame be processed by a server and returned within the time this frame is displayed or 1/30th of a second. The ability to acquire, process, and visualize data in real time is essential for the performance of complex tasks as well as minimizing risk to the patient. We have implemented and compared performance of compression, segmentation and registration algorithms on Clemson\u27s Palmetto supercomputer using dual Nvidia graphics processing units (GPUs) per node and compute unified device architecture (CUDA) programming model. We developed three separate applications that run simultaneously: video acquisition, image processing, and video display. The image processing application allows several algorithms to run simultaneously on different cluster nodes and transfer images through message passing interface (MPI). Our segmentation and registration algorithms resulted in an acceleration factor of around 2 and 8 times respectively. To achieve a higher frame rate, we also resized images and reduced the overall processing time. As a result, using high-speed network to access computing clusters with GPUs to implement these algorithms in parallel will improve surgical procedures by providing real-time medical image processing and laparoscopic data

From Cellular Transport to Synthetic Biomimetic Transport using Carbon Nanotube - Actin Hybrid Assemblies

One of the many interesting materials that have emerged in the field of nanotechnology in the last quarter century is Carbon Nanotube (CNT). CNTs have been explored in a broad range of fields from electronic devices and biosensors, to bioimaging and tissue engineering. However, as stand-alone materials CNTs have limited capabilities in the field of biology and medicine unless they are combined with biological agents. Due to the similarity in diameters, CNTs can be combined with biomolecules such as enzymes, antibodies, antigens, DNA, etc. These hybrid assemblies will combine the properties of the CNTs with the recognition characteristics and functions of the biomolecules.;In our work we utilize one such biomolecule -- actin, which is present in almost all eukaryotic cells and serves as scaffold for molecular motor myosin. The results of the research indicate that actin monomers (G-actins) were able to attach to Multi-Walled Carbon Nanotubes (MWCNTs). The MWCNTs exhibited close to full coverage by the Gactin proteins. Moreover, the G-actins remained functional and were able to polymerize into actin filaments (F-actin) onto the MWCNT scaffolds. Furthermore, the functionality of actin filaments on the surface of the MWCNTs was also investigated. The CNT-Factin hybrid assemblies showed limited movement in synthetic environment. This may be partially due to the inability of the myosin motors to recognize the polarity of the actin filaments, or due to steric hindrance and orientation of actin-based hybrids. The results of our work indicate that these hybrid assemblies can be useful for future biosensor applications with the protein acting as an agent for specific detection

Midazolam-induced learning and memory impairment is modulated by cannabinoid CB1 receptor agonist and antagonist

Background: Memory impairment is a well-known effect of many benzodiazepine compounds which is mediated through their action on gamma-aminobutyric acid type A (GABAA) receptors. On the other hand, cannabinoids can affect learning and memory process through presynaptic modulation of the release of both excitatory glutamate and inhibitory GABA transmitters in brain regions involved in learning and memory. The aim of the present study was to investigate the effect of cannabinoids on memory impairment and long-term potentiation (LTP) reduction properties of the short acting benzodiazepine midazolam.Materials and Methods: One week after insertion of guide cannula by stereotaxic surgery, cannabinoid compounds or midazolam were administered by intracerebroventricular (i.c.v.) injection into lateral ventricle of male rats. Spatial memory task was evaluated using Morris water maze (MWM) test. Electrophysiological evaluation was done by field potential recording of hippocampal neurons in unconscious rats.Results: In MWM test, while i.c.v. administration of AM251 (200 and 500 ng) per se could not change learning and memory function in rats, pretreatment of rats with AM251 (500 ng; i.c.v.) attenuated midazolam-induced memory impairment. In field potential recording, while i.c.v. administration of AM251 (500 ng) and WIN55212-2 (10 μg) did not have any effect on population spike amplitude, pretreatment of rats with both AM251 and WIN55212-2 significantly diminished midazolam-induced PS amplitude reduction in hippocampal neurons.Conclusion: OurOur results suggest the involvement of cannabinoid CB1 receptors in both memory impairment and LTP reduction in hippocampal neurons which was produced by midazolam. This interaction is likely through their effect on both GABAergic and glutamatergic receptors in hippocampus

The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments

The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D image data from particle physics experiments to classify signal from background. In this work we present an extensive convolutional neural architecture search, achieving high accuracy for signal/background discrimination for a HEP classification use-case based on simulated data from the Ice Cube neutrino observatory and an ATLAS-like detector. We demonstrate among other things that we can achieve the same accuracy as complex ResNet architectures with CNNs with less parameters, and present comparisons of computational requirements, training and inference times.Comment: Contribution to Proceedings of CHEP 2019, Nov 4-8, Adelaide, Australi

Influence of arbuscular mycorrhiza fungi, rice-husk-drived biochar and compost on dry matter yield, nutrients uptake and secondary metabolites responses of Iranian borage Echium amoenum Fisch & C. A. Mey

In einem Gewächshausversuch wurde der Einfluss von Bio-Düngern wie vesikulär-arbuskulärer Mykorrhiza, Biochar aus Reisspreu und Biokompost auf Ertrag, Nährstoffaufnahme und sekundäre Inhaltstoffe der Medizinalpflanze Echium amoneum; Fisch & C. A. Mey (iranisches Gurkenkraut) geprüft. Die Varianten waren komplett randomisiert. Alle Behandlungen zeigten signifikante Effekte auf Trockenmasse, Nährstoffaufnahme und Gehalte an Chlorophyll, Carotinoiden, Prolin, Anthocyanen, Flavonoiden, Schleimstoffen und Kohlenhydraten.This study was carried out to investigate the effect of bio-fertilizers including mycorrhiza (MY), rice husk compost (RHC), and biochar (RHB) on dry matter yield, nutrients uptake and some secondary metabolites of the medicinal plant Echium amoenum Fisch & C. A. Mey. The experiment was conducted in a completely randomized design and executed with six treatments and six replications. Treatments comprised of T1: control, T2: MY, T3: RHC, T4: RHB, T5: RHC+MY and T6: RHB+MY. The following parameters were studied: leaf dry weight, macro and micro nutrient uptake, chlorophyll a, chlorophyll b, carotenoids, proline, anthocyanin, flavonoid, mucilage and carbohydrate content. The results show that application of RHC, RHB and MY individually or in combination significantly affected the studied parameters in comparison with the control treatment. In all cases, combined appli­cation of bio-fertilizers together with mycorrhiza application (T5 and T6) had a more positive impact on the studied parameters compared to the application of each treatment alone

The effect of arbuscular mycorrhiza, rice husk compost and biochar on Iranian borage Echium amoenum Fisch & C. A. Mey and post-harvesting soil properties

In einem Gewächshausversuch wurde der Einfluss von Bio-Düngern, wie vesikulär-arbuskulärer Mykorrhiza, Compost und Biochar aus Azolla-Algen auf Ertrag, Ertragsstruktur sowie die Aufnahme an Haupt- und Spurenelementen von iranischem Gurkenkraut geprüft. Gegenstand der Untersuchung war auch der Nährstoffgehalt der Böden nach der Ernte, sowie deren biologische Aktivität. Alle geprüften Behandlungen zeigten im Vergleich zu den Kontrollen signifikante Effekte auf Ertrag und Nährstoffaufnahme. Höhere Bodenatmung und eine höhere mikrobielle Biomasse indizieren eine Steigerung der Fruchtbarkeit der Böden durch die Behandlungen. DOI: 10.5073/JfK.2019.01.02, https://doi.org/10.5073/JfK.2019.01.02This study was conducted to investigate the effect of rice husk compost (RHC), rice husk biochar (RHB) and mycorrhization (MY) on some properties of Iranian Echium amoenum Fisch & C. A. Mey and also on some selected post-harvesting soil properties. A completely randomized design experiment was conducted with six treatments and six replications. Treatments comprised T1: control, T2: MY, T3: RHC, T4: RHB, T5: RHC + MY and T6: RHB + MY. Studied parameters included; shoot and root fresh weights, root and leaf length, shrub height, leaf number, shoot and root NPK content, shoot and root Fe, Zn, Cu and Mn concentration, root colonization percentage, soil NPK status, soil micronutrients concentrations, soil respiration and microbial biomass. Results revealed that application of RHC, RHB and MY individually or in combination with other treatments significantly affected studied parameters. In all cases except for root colonization, combined application (T5 and T6) had more satisfied impacts compared with a single application of treatments. DOI: 10.5073/JfK.2019.01.02, https://doi.org/10.5073/JfK.2019.01.0

Impacts of PGPR, compost and biochar of Azolla on dry matter yield, nutrient uptake, physiological parameters and essential oil of Rosmarinus officinalis L.

In einem Gewächshausversuch wurde der Einfluss von Bio-Düngern wie PGPR, Compost und Biochar aus Azolla-Algen auf Ertrag, Nährstoffaufnahme und diverse Inhaltstoffe der Gewürzpflanze Rosmarin geprüft. Alle Behandlungen zeigten im Vergleich zu den Kontrollen sig­nifikante Effekte auf Ertrag, Nährstoffaufnahme und Gehalte an Chlorophyll, Carotinoiden, Flavonoiden, Kohlenhydraten, Prolin und essentielle Ölen. DOI: 10.5073/JfK.2019.01.01, https://doi.org/10.5073/JfK.2019.01.01Rosemary is one of the most important medicinal plants. In order to study the effect of plant growth promoting rhizobacteria (PGPR), Azolla compost and Azolla biochar on dry matter, nutrient uptake, physiological parameters and essential oil of rosemary, a greenhouse experiment was conducted in a completely randomized design with 6 replications. Treatments consisted of T1 (control), T2 (1% (1 g 100 g-1 dry soil) Azolla compost), T3 (1% Azolla biochar), T4 (PGPR (P. fluorescens)), T5 (1% compost + PGPR) and T6 (1% biochar + PGPR). Results indicated a significant enhancement of dry matter, nutrient uptake, photosynthetic pigments, carbohydrate, flavonoid and essential oil contents of rosemary influenced by organic fertilizers compared to control, particularly with co-appli­cation of PGPR + compost or biochar. Proline content decreased in all treatments in comparison with control. Results indicated positive impacts of PGPR, compost and boichar of Azolla on rosemary production by increasing nutrient uptake and protecting chlorophyll from degradation and enhancing its content in leaves. DOI: 10.5073/JfK.2019.01.01, https://doi.org/10.5073/JfK.2019.01.0

The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments

The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D image data from particle physics experiments to classify signal from background. In this work we present an extensive convolutional neural architecture search, achieving high accuracy for signal/background discrimination for a HEP classification use-case based on simulated data from the Ice Cube neutrino observatory and an ATLAS-like detector. We demonstrate among other things that we can achieve the same accuracy as complex ResNet architectures with CNNs with less parameters, and present comparisons of computational requirements, training and inference times