The GPU on the simulation of cellular computing models

Membrane Computing is a discipline aiming to abstract formal computing models, called membrane systems or P systems, from the structure and functioning of the living cells as well as from the cooperation of cells in tissues, organs, and other higher order structures. This framework provides polynomial time solutions to NP-complete problems by trading space for time, and whose efficient simulation poses challenges in three different aspects: an intrinsic massively parallelism of P systems, an exponential computational workspace, and a non-intensive floating point nature. In this paper, we analyze the simulation of a family of recognizer P systems with active membranes that solves the Satisfiability problem in linear time on different instances of Graphics Processing Units (GPUs). For an efficient handling of the exponential workspace created by the P systems computation, we enable different data policies to increase memory bandwidth and exploit data locality through tiling and dynamic queues. Parallelism inherent to the target P system is also managed to demonstrate that GPUs offer a valid alternative for high-performance computing at a considerably lower cost. Furthermore, scalability is demonstrated on the way to the largest problem size we were able to run, and considering the new hardware generation from Nvidia, Fermi, for a total speed-up exceeding four orders of magnitude when running our simulations on the Tesla S2050 server.     

Fuzzy ontologies-based user profiles applied to enhance e-learning activities

Nowadays, the impact of technological developments on improving human activities is becoming more evident. In e-learning, this situation is no different. There are common to use systems that assist the daily activities of students and teachers. Typically, e-learning recommender systems are focused on students; however, teachers can also benefit from these type of tools. A recommender system can propose actions and resources that facilitate teaching activities like structuring learning strategies. In any case, a complete user’s representation is required. This paper shows how a fuzzy ontology can be used to represent user profiles into a recommender engine and enhances the user’s activities into e-learning environments. A fuzzy ontology is an extension of domain ontologies for solving the problems of uncertainty in sharing and reusing knowledge on the Semantic Web. The user profile is built from learning objects published by the user himself into a learning object repository. The initial experiment confirms that the automatically obtained fuzzy ontology is a good representation of the user’s preferences. The experiment results also indicate that the presented approach is useful and warrants further research in recommending and retrieval information.     

Optimal configuration of Energy-Efficient Ethernet

The IEEE 802.3az standard provides a new low power mode that Ethernet network interfaces can use to save energy when there is no traffic to transmit. Simultaneously with the final standard approval, several algorithms were proposed to govern the physical interface state transition between the normal active mode and the new low power mode. In fact, the standard leaves this sleeping algorithm unspecified to spur competition among different vendors and achieve the greatest energy savings. In this paper, we try to bring some light to the most well known sleeping algorithms, providing mathematical models for the expected energy savings and the average packet delay inflicted on outgoing traffic. We will then use the models to derive optimum configuration parameters for them under given efficiency constraints.     

Molecular Aspects and Prognostic Significance of Microcalcifications in Human Pathology: A Narrative Review

The presence of calcium deposits in human lesions is largely used as imaging biomarkers of human diseases such as breast cancer. Indeed, the presence of micro- or macrocalcifications is frequently associated with the development of both benign and malignant lesions. Nevertheless, the molecular mechanisms involved in the formation of these calcium deposits, as well as the prognostic significance of their presence in human tissues, have not been completely elucidated. Therefore, a better characterization of the biological process related to the formation of calcifications in different tissues and organs, as well as the understanding of the prognostic significance of the presence of these calcium deposits into human tissues could significantly improve the management of patients characterized by microcalcifications associated lesions. Starting from these considerations, this narrative review highlights the most recent histopathological and molecular data concerning the formation of calcifications in breast, thyroid, lung, and ovarian diseases. Evidence reported here could deeply change the current point of view concerning the role of ectopic calcifications in the progression of human diseases and also in the patients’ management. In fact, the presence of calcifications can suggest an unfavorable prognosis due to dysregulation of normal tissues homeostasis.     

Proliferation to Apoptosis Tumor Cell Ratio as a Biomarker to Improve Clinical Management of Pre-Malignant and Symptomatic Plasma Cell Neoplasms

Proliferation and apoptosis of neoplastic cells are prognostic biomarkers in plasma cell neoplasms (PCNs). The prognostic capacity of proliferation to apoptosis ratio (Ratio-PA) in the era of immunomodulatory treatments is re-evaluated in 316 gammopathy of undetermined significance (MGUS), 57 smoldering multiple myeloma (SMM), and 266 multiple myeloma (MM) patients. Ratio-PA of 0.77 ± 0.12, 1.94 ± 0.52, and 11.2 ± 0.7 (p < 0.0001) were observed in MGUS, SMM, and MM patients. Ten-year overall survival (10y-OS) rates for patients with low/high Ratio-PA were 93.5%/77.3% p < 0.0001) for MGUS, 82.5%/64.7% (p < 0.05) for SMM, and 62.3%/47.0% (p < 0.05) for MM. For patients with low, intermediate, and high risk, 10y-OS for low/high Ratio-PA were 95.5%/72.9% (p < 0.0001), 74.2%/50.4% (p < 0.0001), and 35.3%/20.0% (p = 0.836), respectively. Ratio-PA was an independent prognostic factor for OS (HR = 2.119, p < 0.0001, Harrell-C-statistic = 0.7440 ± 0.0194) when co-analyzed with sex, age, and standard risk. In patients with Ratio-PA^high, only first-line therapy with VRd/VTd, but not PAD/VCD, coupled with ASCT was associated with high 10y-OS (82.7%). Tumor cell Ratio-PA estimated at diagnosis offers a prognostic biomarker that complements standard risk stratification and helps to guide the clinical management of pre-malignant and symptomatic PCNs. Every effort should be made to provide first-line therapies including VTd or VRd associated with ASCT to patients with Ratio-PA^high at higher risk of progression and death.     

Validation of a Gas Chromatography-Mass Spectrometry Method for the Measurement of the Redox State Metabolic Ratios Lactate/Pyruvate and β-Hydroxybutyrate/Acetoacetate in Biological Samples

The metabolic ratios lactate/pyruvate and β-hydroxybutyrate/acetoacetate are considered valuable tools to evaluate the in vivo redox cellular state by estimating the free NAD+/NADH in cytoplasm and mitochondria, respectively. The aim of the current study was to validate a gas-chromatography mass spectrometry method for simultaneous determination of the four metabolites in plasma and liver tissue. The procedure included an o-phenylenediamine microwave-assisted derivatization, followed by liquid-liquid extraction with ethyl acetate and silylation with bis(trimethylsilyl)trifluoroacetamide:trimethylchlorosilane 99:1. The calibration curves presented acceptable linearity, with a limit of quantification of 0.001 mM for pyruvate, β-hydroxybutyrate and acetoacetate and of 0.01 mM for lactate. The intra-day and inter-day accuracy and precision were within the European Medicines Agency’s Guideline specifications. No significant differences were observed in the slope coefficient of three-point standard metabolite-spiked curves in plasma or liver and water, and acceptable recoveries were obtained in the metabolite-spiked samples. Applicability of the method was tested in precision-cut liver rat slices and also in HepG2 cells incubated under different experimental conditions challenging the redox state. In conclusion, the validated method presented good sensitivity, specificity and reproducibility in the quantification of lactate/pyruvate and β-hydroxybutyrate/acetate metabolites and may be useful in the evaluation of in vivo redox states.     

A Dual Physical Cross-Linking Strategy to Construct Tough Hydrogels with High Strength,Excellent Fatigue Resistance,and Stretching-Induced Strengthening Effect

Hydrogels with excellent stiffness, toughness, anti-fatigue, and self-recovery properties are regarded as promising water-containing materials. In this work, a dual physically cross-linked (DPC) sodium alginate (SA)/poly[acrylamide (AAm)-acrylic acid (AAc)-octadecyl methacrylate (OMA)]-Fe³⁺ hydrogel is reported, which is constructed by hydrophobic association (HA) and ionic coordination (IC). The optimal DPC hydrogel demonstrates excellent mechanical performance: tensile modulus of 0.65 MPa, tensile strength of 3.31 MPa, elongation at break of 1547%, and toughness of 27.8 MJ m^–3. SA/P(AAm-AAc-OMA)-Fe³⁺ DPC hydrogels also exhibit prominent anti-fatigue and self-recovery performance (99.1–109.7% modulus recovery and 90.4–108.9% dissipated energy recovery after resting for 5 min without additional stimuli at ambient temperature) through the reconstruction of reversible physical cross-linking. Some of the SA/P(AAm-AAc-OMA)-Fe³⁺ DPC hydrogels even exhibit a stretching-induced strengthening effect, which is similar to the performance of muscle—“the more training, the more strength.” Hence, the combination of HA and IC will provide an effective approach to design DPC hydrogels with desirable mechanical performances and a longer service life for wider applications of soft materials.     

Kinetics of the Initial Stage of Sintering from Shrinkage Data: Simultaneous Determination of Activation Energy and Kinetic Model from a Single Nonisothermal Experiment

A new method for the kinetic analysis of the initial stage of sintering for constant-heating-rate data has been proposed. Unlike all of the methods previously reported in the literature, this new method proposed here allows the simultaneous determination of the activation energy and the kinetic model from a single dilatometric curve recorded under a linear-heating-rate program. The proposed method has been tested with simulated sintering curves and experimental results have been obtained for the sintering of a rutile sample.     

Surface Pretreatments of Silicon Nitride for CVD Diamond Deposition

The efficiency of different surface pretreatments (four standard chemical etchings and four diamond powder abrasive procedures) on silicon nitride (Si₃N₄) substrates for chemical vapor deposition (CVD) of diamond has been systematically investigated. Blank Si₃N₄ samples were polished with colloidal silica (∼0.25 μm). Diamond nucleation and growth runs were conducted in a microwave plasma chemical vapor deposition apparatus for 10 min and 6 h, respectively. Superior results concerning nucleation density ( N _d∼ 10¹⁰ cm⁻² after 10 min), film uniformity, and grain size (below 2 μm after 6 h) were obtained for the mechanically microflawed samples, revealing that chemical etchings (hot and cold strong acids, molten base or CF₄ plasma) are not crucial for good CVD diamond quality on Si₃N₄.     

Fast finite difference Poisson solvers on heterogeneous architectures

In this paper we propose and evaluate a set of new strategies for the solution of three dimensional separable elliptic problems on CPU–GPU platforms. The numerical solution of the system of linear equations arising when discretizing those operators often represents the most time consuming part of larger simulation codes tackling a variety of physical situations. Incompressible fluid flows, electromagnetic problems, heat transfer and solid mechanic simulations are just a few examples of application areas that require efficient solution strategies for this class of problems. GPU computing has emerged as an attractive alternative to conventional CPUs for many scientific applications. High speedups over CPU implementations have been reported and this trend is expected to continue in the future with improved programming support and tighter CPU–GPU integration. These speedups by no means imply that CPU performance is no longer critical. The conventional CPU-control–GPU-compute pattern used in many applications wastes much of CPU’s computational power. Our proposed parallel implementation of a classical cyclic reduction algorithm to tackle the large linear systems arising from the discretized form of the elliptic problem at hand, schedules computing on both the GPU and the CPUs in a cooperative way. The experimental result demonstrates the effectiveness of this approach.