Université catholique de Louvain

About: Université catholique de Louvain is a education organization based out in Louvain-la-Neuve, Belgium. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 25319 authors who have published 57360 publications receiving 2172080 citations. The organization is also known as: University of Louvain & UCLouvain.

Prevention and early detection of prostate cancer

Abstract: Prostate cancer is a common malignancy in men and the worldwide burden of this disease is rising. Lifestyle modifications such as smoking cessation, exercise, and weight control offer opportunities to reduce the risk of developing prostate cancer. Early detection of prostate cancer by prostate-specific antigen (PSA) screening is controversial, but changes in the PSA threshold, frequency of screening, and the use of other biomarkers have the potential to minimise the overdiagnosis associated with PSA screening. Several new biomarkers for individuals with raised PSA concentrations or those diagnosed with prostate cancer are likely to identify individuals who can be spared aggressive treatment. Several pharmacological agents such as 5α-reductase inhibitors and aspirin could prevent development of prostate cancer. In this Review, we discuss the present evidence and research questions regarding prevention, early detection of prostate cancer, and management of men either at high risk of prostate cancer or diagnosed with low-grade prostate cancer.

The Logarithmic ACD Model: An Application to the Bid-Ask Quote Process of Three NYSE Stocks

Abstract: This paper introduces the logarithmic autoregressive conditional duration (Log-ACD) model and compares it with the ACD model of ENGLE and RUSSELL [1998]. The logarithmic version allows to introduce in the model additional variables without sign restrictions on their coefficients. We apply the Log-ACD model to price durations relative to the bid-ask quote process of three securities listed on the New York Stock Exchange, and we investigate the influence of some characteristics of the trade process (trading intensity, average volume per trade and average spread) on the bid-ask quote process.

Isolation of Adherent Polycyclic Aromatic Hydrocarbon (PAH)-Degrading Bacteria Using PAH-Sorbing Carriers

Abstract: Two different procedures were compared to isolate polycyclic aromatic hydrocarbon (PAH)-utilizing bacteria from PAH-contaminated soil and sludge samples, i.e., (i) shaken enrichment cultures in liquid mineral medium in which PAHs were supplied as crystals and (ii) a new method in which PAH degraders were enriched on and recovered from hydrophobic membranes containing sorbed PAHs. Both techniques were successful, but selected from the same source different bacterial strains able to grow on PAHs as the sole source of carbon and energy. The liquid enrichment mainly selected for Sphingomonas spp., whereas the membrane method exclusively led to the selection of Mycobacterium spp. Furthermore, in separate membrane enrichment set-ups with different membrane types, three repetitive extragenic palindromic PCR-related Mycobacterium strains were recovered. The new Mycobacterium isolates were strongly hydrophobic and displayed the capacity to adhere strongly to different surfaces. One strain, Mycobacterium sp. LB501T, displayed an unusual combination of high adhesion efficiency and an extremely high negative charge. This strain may represent a new bacterial species as suggested by 16S rRNA gene sequence analysis. These results indicate that the provision of hydrophobic sorbents containing sorbed PAHs in the enrichment procedure discriminated in favor of certain bacterial characteristics. The new isolation method is appropriate to select for adherent PAH-degrading bacteria, which might be useful to biodegrade sorbed PAHs in soils and sludge.

Vowel recognition with four coupled spin-torque nano-oscillators

Abstract: In recent years, artificial neural networks have become the flagship algorithm of artificial intelligence1. In these systems, neuron activation functions are static, and computing is achieved through standard arithmetic operations. By contrast, a prominent branch of neuroinspired computing embraces the dynamical nature of the brain and proposes to endow each component of a neural network with dynamical functionality, such as oscillations, and to rely on emergent physical phenomena, such as synchronization2–6, for solving complex problems with small networks7–11. This approach is especially interesting for hardware implementations, because emerging nanoelectronic devices can provide compact and energy-efficient nonlinear auto-oscillators that mimic the periodic spiking activity of biological neurons12–16. The dynamical couplings between oscillators can then be used to mediate the synaptic communication between the artificial neurons. One challenge for using nanodevices in this way is to achieve learning, which requires fine control and tuning of their coupled oscillations17; the dynamical features of nanodevices can be difficult to control and prone to noise and variability18. Here we show that the outstanding tunability of spintronic nano-oscillators—that is, the possibility of accurately controlling their frequency across a wide range, through electrical current and magnetic field—can be used to address this challenge. We successfully train a hardware network of four spin-torque nano-oscillators to recognize spoken vowels by tuning their frequencies according to an automatic real-time learning rule. We show that the high experimental recognition rates stem from the ability of these oscillators to synchronize. Our results demonstrate that non-trivial pattern classification tasks can be achieved with small hardware neural networks by endowing them with nonlinear dynamical features such as oscillations and synchronization. A network of four spin-torque nano-oscillators can be trained in real time to recognize spoken vowels, in a simple and scalable approach that could be exploited for large-scale neural networks.