Simulating the behavior of the human brain on GPUS

The simulation of the behavior of the Human Brain is one of the most important challenges in computing today. The main problem consists of finding efficient ways to manipulate and compute the huge volume of data that this kind of simulations need, using the current technology. In this sense, this work is focused on one of the main steps of such simulation, which consists of computing the Voltage on neurons’ morphology. This is carried out using the Hines Algorithm and, although this algorithm is the optimum method in terms of number of operations, it is in need of non-trivial modifications to be efficiently parallelized on GPUs. We proposed several optimizations to accelerate this algorithm on GPU-based architectures, exploring the limitations of both, method and architecture, to be able to solve efficiently a high number of Hines systems (neurons). Each of the optimizations are deeply analyzed and described. Two different approaches are studied, one for mono-morphology simulations (batch of neurons with the same shape) and one for multi-morphology simulations (batch of neurons where every neuron has a different shape). In mono-morphology simulations we obtain a good performance using just a single kernel to compute all the neurons. However this turns out to be inefficient on multi-morphology simulations. Unlike the previous scenario, in multi-morphology simulations a much more complex implementation is necessary to obtain a good performance. In this case, we must execute more than one single GPU kernel. In every execution (kernel call) one specific part of the batch of the neurons is solved. These parts can be seen as multiple and independent tridiagonal systems. Although the present paper is focused on the simulation of the behavior of the Human Brain, some of these techniques, in particular those related to the solving of tridiagonal systems, can be also used for multiple oil and gas simulations. Our studies have proven that the optimizations proposed in the present work can achieve high performance on those computations with a high number of neurons, being our GPU implementations about 4× and 8× faster than the OpenMP multicore implementation (16 cores), using one and two NVIDIA K80 GPUs respectively. Also, it is important to highlight that these optimizations can continue scaling, even when dealing with a very high number of neurons.This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA1), from the Spanish Ministry of Economy and Competitiveness under the project Computación de Altas Prestaciones VII (TIN2015-65316-P), the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programació i Entorns d’Execució Parallels (2014-SGR-1051). We thank the support of NVIDIA through the BSC/UPC NVIDIA GPU Center of Excellence, and the European Union’s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement No. 749516.Peer ReviewedPostprint (published version

Kurcuma: a kitchen utensil recognition collection for unsupervised domain adaptation

The use of deep learning makes it possible to achieve extraordinary results in all kinds of tasks related to computer vision. However, this performance is strongly related to the availability of training data and its relationship with the distribution in the eventual application scenario. This question is of vital importance in areas such as robotics, where the targeted environment data are barely available in advance. In this context, domain adaptation (DA) techniques are especially important to building models that deal with new data for which the corresponding label is not available. To promote further research in DA techniques applied to robotics, this work presents Kurcuma (Kitchen Utensil Recognition Collection for Unsupervised doMain Adaptation), an assortment of seven datasets for the classification of kitchen utensils—a task of relevance in home-assistance robotics and a suitable showcase for DA. Along with the data, we provide a broad description of the main characteristics of the dataset, as well as a baseline using the well-known domain-adversarial training of neural networks approach. The results show the challenge posed by DA on these types of tasks, pointing to the need for new approaches in future work.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by the I+D+i project TED2021-132103A-I00 (DOREMI), funded by MCIN/AEI/10.13039/501100011033. Some of the computing resources were provided by the Generalitat Valenciana and the European Union through the FEDER funding program (IDIFEDER/2020/003). The second author is supported by grant APOSTD/2020/256 from “Programa I+D+i de la Generalitat Valenciana”

Reconstruction of a Genome-Scale Metabolic Model of Streptomyces albus J1074: Improved Engineering Strategies in Natural Product Synthesis

Streptomyces albus J1074 is recognized as an effective host for heterologous production of natural products. Its fast growth and efficient genetic toolbox due to a naturally minimized genome have contributed towards its advantage in expressing biosynthetic pathways for a diverse repertoire of products such as antibiotics and flavonoids. In order to develop precise model-driven engineering strategies for de novo production of natural products, a genome-scale metabolic model (GEM) was reconstructed for the microorganism based on protein homology to model species Streptomyces coelicolor while drawing annotated data from databases and literature for further curation. To demonstrate its capabilities, the Salb-GEM was used to predict overexpression targets for desirable compounds using flux scanning with enforced objective function (FSEOF). Salb-GEM was also utilized to investigate the effect of a minimized genome on metabolic gene essentialities in comparison to another Streptomyces species, S. coelicolor

cuHinesBatch: solving multiple hines systems on GPUs Human Brain Project

The simulation of the behavior of the Human Brain is one of the most important challenges today in computing. The main problem consists of finding efficient ways to manipulate and compute the huge volume of data that this kind of simulations need, using the current technology. In this sense, this work is focused on one of the main steps of such simulation, which consists of computing the Voltage on neurons’ morphology. This is carried out using the Hines Algorithm. Although this algorithm is the optimum method in terms of number of operations, it is in need of non-trivial modifications to be efficiently parallelized on NVIDIA GPUs. We proposed several optimizations to accelerate this algorithm on GPU-based architectures, exploring the limitations of both, method and architecture, to be able to solve efficiently a high number of Hines systems (neurons). Each of the optimizations are deeply analyzed and described. To evaluate the impact of the optimizations on real inputs, we have used 6 different morphologies in terms of size and branches. Our studies have proven that the optimizations proposed in the present work can achieve a high performance on those computations with a high number of neurons, being our GPU implementations about 4× and 8× faster than the OpenMP multicore implementation (16 cores), using one and two K80 NVIDIA GPUs respectively. Also, it is important to highlight that these optimizations can continue scaling even when dealing with number of neurons.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 720270 (HBP SGA1), from the Spanish Ministry of Economy and Competitiveness under the project Computación de Altas Prestaciones VII (TIN2015-65316-P) and the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programació i Entorns d’Execució Paral·lels (2014-SGR-1051). We thank the support of NVIDIA through the BSC/UPC NVIDIA GPU Center of Excellence. Antonio J. Peña is cofinanced by the Spanish Ministry of Economy and Competitiveness under Juan de la Cierva fellowship number IJCI-2015-23266.Peer ReviewedPostprint (published version

High plasticity of axonal pathology in Alzheimer's disease mouse models

Axonal dystrophies (AxDs) are swollen and tortuous neuronal processes that are associated with extracellular depositions of amyloid beta (Abeta) and have been observed to contribute to synaptic alterations occurring in Alzheimer's disease. Understanding the temporal course of this axonal pathology is of high relevance to comprehend the progression of the disease over time. We performed a long-term in vivo study (up to 210 days of two-photon imaging) with two transgenic mouse models (dE9xGFP-M and APP-PS1xGFP-M). Interestingly, AxDs were formed only in a quarter of GFP-expressing axons near Abeta-plaques, which indicates a selective vulnerability. AxDs, especially those reaching larger sizes, had long lifetimes and appeared as highly plastic structures with large variations in size and shape and axonal sprouting over time. In the case of the APP-PS1 mouse only, the formation of new long axonal segments in dystrophic axons (re-growth phenomenon) was observed. Moreover, new AxDs could appear at the same point of the axon where a previous AxD had been located before disappearance (re-formation phenomenon). In addition, we observed that most AxDs were formed and developed during the imaging period, and numerous AxDs had already disappeared by the end of this time. This work is the first in vivo study analyzing quantitatively the high plasticity of the axonal pathology around Abeta plaques. We hypothesized that a therapeutically early prevention of Abeta plaque formation or their growth might halt disease progression and promote functional axon regeneration and the recovery of neural circuits

High plasticity of axonal pathology in Alzheimer's disease mouse models

Axonal dystrophies (AxDs) are swollen and tortuous neuronal processes that are associated with extracellular depositions of amyloid beta (Abeta) and have been observed to contribute to synaptic alterations occurring in Alzheimer's disease. Understanding the temporal course of this axonal pathology is of high relevance to comprehend the progression of the disease over time. We performed a long-term in vivo study (up to 210 days of two-photon imaging) with two transgenic mouse models (dE9xGFP-M and APP-PS1xGFP-M). Interestingly, AxDs were formed only in a quarter of GFP-expressing axons near Abeta-plaques, which indicates a selective vulnerability. AxDs, especially those reaching larger sizes, had long lifetimes and appeared as highly plastic structures with large variations in size and shape and axonal sprouting over time. In the case of the APP-PS1 mouse only, the formation of new long axonal segments in dystrophic axons (re-growth phenomenon) was observed. Moreover, new AxDs could appear at the same point of the axon where a previous AxD had been located before disappearance (re-formation phenomenon). In addition, we observed that most AxDs were formed and developed during the imaging period, and numerous AxDs had already disappeared by the end of this time. This work is the first in vivo study analyzing quantitatively the high plasticity of the axonal pathology around Abeta plaques. We hypothesized that a therapeutically early prevention of Abeta plaque formation or their growth might halt disease progression and promote functional axon regeneration and the recovery of neural circuits

Docetaxel plus cisplatin is effective for patients with metastatic breast cancer resistant to previous anthracycline treatment: a phase II clinical trial

BACKGROUND: Patients with metastatic breast cancer (MBC) are frequently exposed to high cumulative doses of anthracyclines and are at risk of resistance and cardiotoxicity. This phase II trial evaluated the efficacy and toxicity of docetaxel plus cisplatin, as salvage chemotherapy in patients with MBC resistant to prior anthracyclines. METHODS: Patients with MBC that had progressed after at least one prior chemotherapy regimen containing anthracyclines received docetaxel 75 mg/m(2 )followed by cisplatin 60 mg/m(2 )every 3 weeks for a maximum of 6 cycles or until disease progression. RESULTS: Between Jan 2000 and May 2002, 24 patients with tumors primary resistant and 15 with secondary resistant disease were accrued. All 39 patients were evaluable for safety and 36 for efficacy. The objective response rate was 31% (95% CI, 16–45%) with 3 complete responses. The median time to disease progression was 7 months, and the median overall survival was 23 months (median follow-up of 41 months). Neutropenia was the most frequently observed severe hematologic toxicity (39% of patients), whereas asthenia and nausea were the most common non-hematologic toxicities. No treatment-related death was observed. CONCLUSION: In conclusion, we found docetaxel plus cisplatin to be an active and safe chemotherapy regimen for patients with MBC resistant to anthracyclines