Cellular signalling pathways mediating the pathogenesis of chronic inflammatory respiratory diseases: an update

Respiratory disorders, especially non-communicable, chronic inflammatory diseases, are amongst the leading causes of mortality and morbidity worldwide. Respiratory diseases involve multiple pulmonary components, including airways and lungs that lead to their abnormal physiological functioning. Several signaling pathways have been reported to play an important role in the pathophysiology of respiratory diseases. These pathways, in addition, become the compounding factors contributing to the clinical outcomes in respiratory diseases. A range of signaling components such as Notch, Hedgehog, Wingless/Wnt, bone morphogenetic proteins, epidermal growth factor and fibroblast growth factor is primarily employed by these pathways in the eventual cascade of events. The different aberrations in such cell-signaling processes trigger the onset of respiratory diseases making the conventional therapeutic modalities ineffective. These challenges have prompted us to explore novel and effective approaches for the prevention and/or treatment of respiratory diseases. In this review, we have attempted to deliberate on the current literature describing the role of major cell signaling pathways in the pathogenesis of pulmonary diseases and discuss promising advances in the field of therapeutics that could lead to novel clinical therapies capable of preventing or reversing pulmonary vascular pathology in such patients

The ESCAPE project : Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) and ALADIN (Aire Limitee Adaptation Dynamique Developpement International); and COSMO-EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU-GPU arrangements

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

Abstract. In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International); and COSMO–EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU–GPU arrangements

Efficient similarity computations on parallel machines using data shaping

Similarity computation is a fundamental operation in all forms of data. Big Data is, typically, characterized by attributes such as volume, velocity, variety, veracity, etc. In general, Big Data variety appears as structured, semi-structured or unstructured forms. The volume of Big Data in general, and semi-structured data in particular, is increasing at a phenomenal rate. Big Data phenomenon is posing new set of challenges to similarity computation problems occurring in semi-structured data. Technology and processor architecture trends suggest very strongly that future processors shall have ten's of thousands of cores (hardware threads). Another crucial trend is that ratio between on-chip and off-chip memory to core counts is decreasing. State-of-the-art parallel computing platforms such as General Purpose Graphics Processors (GPUs) and MICs are promising for high performance as well high throughput computing. However, processing semi-structured component of Big Data efficiently using parallel computing systems (e.g. GPUs) is challenging. Reason being most of the emerging platforms (e.g. GPUs) are organized as Single Instruction Multiple Thread/Data machines which are highly structured, where several cores (streaming processors) operate in lock-step manner, or they require a high degree of task-level parallelism. We argue that effective and efficient solutions to key similarity computation problems need to operate in a synergistic manner with the underlying computing hardware. Moreover, semi-structured form input data needs to be shaped or reorganized with the goal to exploit the enormous computing power of \textit{state-of-the-art} highly threaded architectures such as GPUs. For example, shaping input data (via encoding) with minimal data-dependence can facilitate flexible and concurrent computations on high throughput accelerators/co-processors such as GPU, MIC, etc. We consider various instances of traditional and futuristic problems occurring in intersection of semi-structured data and data analytics. Preprocessing is an operation common at initial stages of data processing pipelines. Typically, the preprocessing involves operations such as data extraction, data selection, etc. In context of semi-structured data, twig filtering is used in identifying (and extracting) data of interest. Duplicate detection and record linkage operations are useful in preprocessing tasks such as data cleaning, data fusion, and also useful in data mining, etc., in order to find similar tree objects. Likewise, tree edit is a fundamental metric used in context of tree problems; and similarity computation between trees another key problem in context of Big Data. This dissertation makes a case for platform-centric data shaping as a potent mechanism to tackle the data- and architecture-borne issues in context of semi-structured data processing on GPU and GPU-like parallel architecture machines. In this dissertation, we propose several data shaping techniques for tree matching problems occurring in semi-structured data. We experiment with real world datasets. The experimental results obtained reveal that the proposed platform-centric data shaping approach is effective for computing similarities between tree objects using GPGPUs. The techniques proposed result in performance gains up to three orders of magnitude, subject to problem and platform.</p

Association between VEGF polymorphisms (−460 T/C and +936 C/T) and retinopathy of prematurity risk: A meta-analysis

AbstractPurposeVascular endothelial growth factor (VEGF) contributes to the development of retinopathy of prematurity (ROP). A number of studies investigated the association of ROP with VEGF −460 T/C and +936 C/T polymorphisms but the results were conflicting. In order to derive a more precise estimation of the associations, we performed a meta-analysis of the relationship between VEGF −460 T/C and +936 C/T polymorphisms with ROP in all published studies.MethodsA literature search was performed systematically using electronic databases. Published literature from PubMed and other databases was retrieved. The odds ratio (OR) with 95% confidence interval (CI) was used to estimate the pooled effect. Each −460 T/C and +936 C/T polymorphism included four case-control studies including case/control 249/308 and 179/250 respectively.ResultsThrough literature search, we found that both VEGF −460 T/C and +936 C/T polymorphisms were not associated with ROP risk at allelic, co-dominant, dominant and recessive models.ConclusionsThis meta-analysis suggests that the VEGF −460 T/C and +936 C/T polymorphism might contribute to genetic susceptibility of ROP. The association between VEGF −460 T/C and +936 C/T polymorphism and ROP risk awaits further investigation

Point Prevalence Study (PPS) of Antibiotic Usage and Bacterial Culture Rate (BCR) among Secondary Care Hospitals of Small Cities in Central India: Consolidating Indian Evidence

Objective Indian hospitals (especially government-run public sector hospitals) have a nonexistent antimicrobial stewardship program (AMSP). After successfully initiating AMSPs in tertiary care hospitals of India, the Indian Council of Medical Research envisages implementing AMSP in secondary care hospitals. This study is about the baseline data on antibiotic consumption in secondary care hospitals. Materials and Methods It was a prospective longitudinal observational chart review type of study. Baseline data on antibiotic consumption was captured by a 24-hour point prevalence study of antibiotic usage and bacterial culture rate. The prescribed antibiotics were classified according to the World Health Organization (WHO) Access, Watch, and Reserve classification. All data were collated in Microsoft Excel and summarized as percentages. Results Out of the 864 patients surveyed, overall antibiotic usage was 78.9% (71.5% in low-priority areas vs. 92.2% in high-priority areas). Most of the antibiotic usage was empirical with an extremely low bacterial culture rate (21.9%). Out of the prescribed drugs, 53.1% were from the WHO watch category and 5.5% from the reserve category. Conclusion Even after 5 years of the launch of the national action plan on AMR (NAP-AMR) of India, AMSP is still non-existent in small- and medium-level hospitals in urban cities. The importance of trained microbiologists in the health care system is identified as a fulcrum in combating antimicrobial resistance (AMR); however, their absence in government-run district hospitals is a matter of grave concern and needs to be addressed sooner than later