Enzymes and Liquid-Liquid Phase Separation: A New Era for the Regulation of Enzymatic Activity

Liquid-liquid phase separation (LLPS) is recognized as a mechanism for regulation of enzymatic activity. Biochemical mechanisms include concentrating reactants to enhance reaction rates or sequester enzymes and reactants from each other to reduce the reaction rate. On the other hand, LLPS might also regulate the diffusion of small molecules or important parameters for enzymatic activity (such as modulators, macromolecular crowding and changing the media physicochemical features) increasing or decreasing the reaction rate of the enzymes. Furthermore, the co-compartmentalization of specific enzymes can favour or speed up specific metabolic fluxes. Here, we discuss how LLPS contributed to generate a new era for enzyme regulation and the new possible subtle regulation mechanisms still unexplored.journal articl

Simultaneous Detection of H and D NMR Signals in a micro-Tesla Field

We present NMR spectra of remote-magnetized deuterated water, detected in an unshielded environment by means of a differential atomic magnetometer. The measurements are performed in a $\mu$T field, while pulsed techniques are applied -following the sample displacement- in a 100~$\mu$T field, to tip both D and H nuclei by controllable amounts. The broadband nature of the detection system enables simultaneous detection of the two signals and accurate evaluation of their decay times. The outcomes of the experiment demonstrate the potential of ultra-low-field NMR spectroscopy in important applications where the correlation between proton and deuteron spin-spin relaxation rates as a function of external parameters contains significant information.Comment: 7 pages (letter, 4 pages) plus supplemental material as an appendix. This document is the unedited author's version of a Submitted Work that was subsequently accepted for publication in Journal of Phys. Chem. Lett., copyright American Chemical Society after peer review. To access the final edited and published work see: pubs.acs.org/doi/abs/10.1021/acs.jpclett.7b0285

Advances in cancer modeling: fluidic systems for increasing representativeness of large 3D multicellular spheroids

The representativeness of a cellular model is fundamental in pre-clinical cancer studies. Size, heterogeneity and perfusion are three key aspects characterizing the behavior of the tumor and driving its progression. In vitro resemblance of in vivo tumor conditions can be maximized by: (a) using heterogeneous large-sized three-dimensional (3D) multicellular models; (b) utilizing fluidic systems to modulate the culture microenvironment. This work discusses the benefits of using large-sized spheroids as 3D pre-clinical culture models, besides analyzing the microfluidic systems that permit their cultivation and manipulation in dynamic controlled conditions

Recent Trends in Word Sense Disambiguation : A Survey

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CT perfusion in patients with lung cancer: Squamous cell Carcinoma and Adenocarcinoma show a different Blood Flow

Objectives. To characterize tumour baseline blood flow (BF) in two lung cancer subtypes, adenocarcinoma (AC) and squamous cell carcinoma (SCC), also investigating those \u201cborderline\u201d cases whose perfusion value is closer to the group mean of the other histotype. Materials and Methods. 26 patients (age range 36-81 years) with primary Non-Small Cell Lung Cancer (NSCLC), subdivided into 19 AC and 7 SCC, were enrolled in this study and underwent a CT perfusion, at diagnosis. BF values were computed according to the maximum-slope method and unreliable values (e.g., arising from artefacts or vessels) were automatically removed. The one-tail Welch\u2019s t-test (p-value < 0.05) was employed for statistical assessment. Results. At diagnosis, mean BF values (in [mL/min/100g]) of AC group [(83.5 \ub1 29.4)] are significantly greater than those of SCC subtype [(57.0 \ub1 27.2)] (p-value = 0.02). However, two central SCCs undergoing artefacts from vena cava and pulmonary artery have an artificially increased mean BF. Conclusions. The different hemodynamic behaviour of AC and SCC should be considered as a biomarker supporting treatment planning to select the patients, mainly with AC, that would most benefit from antiangiogenic therapies. The significance of results was achieved by automatically detecting and excluding artefactual BF values

Masked unbiased principles for parameter selection in variational image restoration under Poisson noise

In this paper we address the problem of automatically selecting the regularization parameter in variational models for the restoration of images corrupted by Poisson noise. More specifically, we first review relevant existing unmasked selection criteria which fully exploit the acquired data by considering all pixels in the selection procedure. Then, based on an idea originally proposed by Carlavan and Blanc-Feraud to effectively deal with dark backgrounds and/or low photon-counting regimes, we introduce and discuss the masked versions—some of them already existing—of the considered unmasked selection principles formulated by simply discarding the pixels measuring zero photons. However, we prove that such a blind masking strategy yields a bias in the resulting principles that can be overcome by introducing a novel positive Poisson distribution correctly modeling the statistical properties of the undiscarded noisy data. Such distribution is at the core of newly proposed masked unbiased counterparts of the discussed strategies. All the unmasked, masked biased and masked unbiased principles are extensively compared on the restoration of different images in a wide range of photon-counting regimes. Our tests allow to conclude that the novel masked unbiased selection strategies, on average, compare favorably with unmasked and masked biased counterparts

Spin dynamic response to a time dependent field

The dynamic response of a parametric system constituted by a spin precessing in a time dependent magnetic field is studied by means of a perturbative approach that unveils unexpected features, and is then experimentally validated. The first-order analysis puts in evidence different regimes: beside a tailorable low-pass-filter behaviour, a band-pass response with interesting potential applications emerges. Extending the analysis to the second perturbation order permits to study the response to generically oriented fields and to characterize several non-linear features in the behaviour of such kind of systems.Comment: 13 pages, 7 figures, 52 references. Accepted for publication in Applied Physics

A New Approach to Image Reconstruction in Positron Emission Tomography Using Artificial Neural Networks

This study investigates the possibility of using an Artificial Neural Network (ANN) for reconstructing Positron Emission Tomography (PET) images. The network is trained with simulated data which include physical effects such as attenuation and scattering. Once the training ends, the weights of the network are held constant. The network is able to reconstruct every type of source distribution contained inside the area mapped during the learning. The reconstruction of a simulated brain phantom in a noiseless case shows an improvement if compared with Filtered Back-Projection reconstruction (FBP). In noisy cases there is still an improvement, even if we do not compensate for noise fluctuations. These results show that it is possible to reconstruct PET images using ANNs. Initially we used a Dec Alpha; then, due to the high data parallelism of this reconstruction problem, we ported the learning on a Quadrics (SIMD) machine, suited for the realization of a small medical dedicated system. These results encourage us to continue in further studies that will make possible reconstruction of images of bigger dimension than those used in the present work (32 × 32 pixels)