Search

Mollusk shells are often found in archeological sites, given their great preservation potential and high value as a multipurpose resource, and they can often be the only available materials useful for radiocarbon (14C) dating. However, dates obtained from shells are often regarded as less reliable compared to those from bones, wood, or charcoals due to different factors (e.g., Isotope fractionation, reservoir effects etc.). The standard acid etching pretreatment for mollusk shells is the most used in many 14C laboratories, although another protocol known as CarDS (Carbonate Density Separation) was introduced just over a decade ago. We compare these two methods with two newly proposed methods for intracellular organic matrix extraction. We applied all four methods to samples selected from different archeological layers of the well-known Upper Paleolithic site of Vale Boi, rich in mollusk specimens throughout the stratigraphic sequence. Here we compare our results to previous dates to determine which of these pretreatment methods results in the most reliable 14C dates. Based on the results of this study, all four methods gave inconsistent ages compared to previous dates and the stratigraphic attribution. [ABSTRACT FROM AUTHOR]

This paper presents the design, simulation, prototyping, and measurement results of a digitally tunable fourth order Gm-C low-pass filter (LPF) for multi-standard radio receivers. The LPF cut-off frequency can be tunned by digitally selecting the transconductance of the basic reconfigurable operational transconductance amplifiers (OTAs) that compose the circuit. Four operation modes allow for control of the OTA transconductances and, consequently, the scaling of power consumption. The filter was designed and prototyped in a 1.8 V 180 nm CMOS process. The measurement results indicate that the configurability provides a cutoff frequency of 1.90/3.56/6.07/8.15 MHz with a power consumption ranging from 9.9 to 13.1 mW. The designed filter achieves an IIP3 of 8.17 dBm for a signal bandwidth of 8.15 MHz. The performance, in terms of power dissipation, noise, and cut-off frequency, is at the same order of magnitude compared to recent related works reported in the literature. The advantages are a compact area, small sensitivity to component mismatches, and low design complexity. The proposed filter presents electrical characteristics suitable for the application in radio receivers for multi-carrier WCDMA signals. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is a diffuse brain tumor characterized by high infiltration in the brain parenchyma rendering the tumor difficult to eradicate by neurosurgery. Efforts to identify molecular targets involved in the invasive behavior of GBM suggested ion channel inhibition as a promising therapeutic approach. To determine if the Ca(2+)-dependent K(+) channel KCa3.1 could represent a key element for GBM brain infiltration, human GL-15 cells were xenografted into the brain of SCID mice that were then treated with the specific KCa3.1 blocker TRAM-34 (1-((2-chlorophenyl) (diphenyl)methyl)-1H-pyrazole). After 5 weeks of treatment, immunofluorescence analyses of cerebral slices revealed reduced tumor infiltration and astrogliosis surrounding the tumor, compared with untreated mice. Significant reduction of tumor infiltration was also observed in the brain of mice transplanted with KCa3.1-silenced GL-15 cells, indicating a direct effect of TRAM-34 on GBM-expressed KCa3.1 channels. As KCa3.1 channels are also expressed on microglia, we investigated the effects of TRAM-34 on microglia activation in GL-15 transplanted mice and found a reduction of CD68 staining in treated mice. Similar results were observed in vitro where TRAM-34 reduced both phagocytosis and chemotactic activity of primary microglia exposed to GBM-conditioned medium. Taken together, these results indicate that KCa3.1 activity has an important role in GBM invasiveness in vivo and that its inhibition directly affects glioma cell migration and reduces astrocytosis and microglia activation in response to tumor-released factors. KCa3.1 channel inhibition therefore constitutes a potential novel therapeutic approach to reduce GBM spreading into the surrounding tissue.

In this paper, we discuss the estimation of conditional quantiles of aggregate claim amounts for non-life insurance embedding the problem in a quantile regression framework using the neural network approach. As the first step, we consider the quantile regression neural networks (QRNN) procedure to compute quantiles for the insurance ratemaking framework. As the second step, we propose a new quantile regression combined actuarial neural network (Quantile-CANN) combining the traditional quantile regression approach with a QRNN. In both cases, we adopt a two-part model scheme where we fit a logistic regression to estimate the probability of positive claims and the QRNN model or the Quantile-CANN for the positive outcomes. Through a case study based on a health insurance dataset, we highlight the overall better performances of the proposed models with respect to the classical quantile regression one. We then use the estimated quantiles to calculate a loaded premium following the quantile premium principle, showing that the proposed models provide a better risk differentiation. [ABSTRACT FROM AUTHOR]