Collective behaviour in biological systems pitches us against theoretical challenges way beyond the borders of ordinary statistical physics. The lack of concepts like scaling and renormalization is particularly grievous, as it forces us to negotiate with scores of details whose relevance is often hard to assess. In an attempt to improve on this situation, we present here experimental evidence of the emergence of dynamic scaling laws in natural swarms. We find that spatio-temporal correlation functions in different swarms can be rescaled by using a single characteristic time, which grows with the correlation length with a dynamical critical exponent z~1. We run simulations of a model of self-propelled particles in its swarming phase and find z~2, suggesting that natural swarms belong to a novel dynamic universality class. This conclusion is strengthened by experimental evidence of non-exponential relaxation and paramagnetic spin-wave remnants, indicating that previously overlooked inertial effects are needed to describe swarm dynamics. The absence of a purely relaxational regime suggests that natural swarms are subject to a near-critical censorship of hydrodynamics.Comment: 12 pages. 6 figures, 1 table and 2 supplementary video

Lung cancer is the leading cause of cancer deaths worldwide among both men and women, with more than 1 million deaths annually. Non-small cell lung cancer (NSCLC) accounts for about 80% of all lung cancers.Although recent advances have been made in diagnosis and treatment strategies, the prognosis of NSCLC patients is poor and it is basically due to a lack of early diagnostic tools.However, in the last years genetic and biochemical studies have provided more information about the protein and gene's mutations involved in lung tumors. Additionally, recent proteomic and microRNA's approaches have been introduced to help biomarker discovery.Here we would like to discuss the most recent discoveries in lung cancer pathways, focusing on the genetic and epigenetic factors that play a crucial role in malignant cell proliferation, and how they could be helpful in diagnosis and targeted therapy.

Imatinib (IM) is considered the gold standard for chronic myeloid leukemia (CML) treatment, although resistance is emerging as a significant problem. The proinflammatory cytokines interleukin-6 (IL-6) and interleukin-8 (IL-8) play an important role in cell proliferation, survival, and resistance to glucocorticoid-mediated cell death. Several transcription factors such as NF-KB and AP-1 are activated in response to physiopathological increases and modulation of intracellular calcium levels. Our previous study demonstrated that lymphocytes from CML patients showed dysregulated calcium homeostasis and oxidative stress. Alteration in ionized calcium concentration in the cytosol has been implicated in the initiation of secretion, contraction, and cell proliferation. In this study, we hypothesized that IL-6, IL-8, NF-kB, AP-1, and intracellular calcium may be used as selective and prognostic factors to address the follow-up in CML patients treated with imatinib. Our results demonstrated a significant down-regulation in IL-6 and IL-8 release as well as NF-kB and AP-1 activation in lymphomonocytes from Imatinib-treated patients, compared to samples from untreated patients. In parallel, IM treatment, in vivo and in vitro, were able to modulate the intracellular calcium concentration of peripheral blood mononuclear cells of CML patients by acting at the level of InsP(3) receptor in the endoplasmic reticulum and at the level of the purinergic receptors on plasma membrane. The results of this study show that measurements of NF-kB, AP-1, IL-6, IL-8, and intracellular calcium in CML patients treated with Imatinib may give important information to the hematologist on diagnostic criteria and are highly predictive in patients with newly diagnosed CML.

Spiking neural networks (SNNs) employing memristive synapses are capable of life-long online learning. Because of their ability to process and classify large amounts of data in real-time using compact and low-power electronic systems, they promise a substantial technology breakthrough. However, the critical issue that memristor-based SNNs have to face is the fundamental limitation in their memory capacity due to finite resolution of the synaptic elements, which leads to the replacement of old memories with new ones and to a finite memory lifetime. In this study we demonstrate that the nonlinear conductance dynamics of memristive devices can be exploited to improve the memory lifetime of a network. The network is simulated on the basis of a spiking neuron model of mixed-signal digital-analogue sub-threshold neuromorphic CMOS circuits, and on memristive synapse models derived from the experimental nonlinear conductance dynamics of resistive memory devices when stimulated by trains of identical pulses. The network learning circuits implement a spike-based plasticity rule compatible with both spike-timing and rate-based learning rules. In order to get an insight on the memory lifetime of the network, we analyse the learning dynamics in the context of a classical benchmark of neural network learning, that is hand-written digit classification. In the proposed architecture, the memory lifetime and the performance of the network are improved for memristive synapses with nonlinear dynamics with respect to linear synapses with similar resolution. These results demonstrate the importance of following holistic approaches that combine the study of theoretical learning models with the development of neuromorphic CMOS SNNs with memristive devices used to implement life-long on-chip learning.

BACKGROUND:The determination of cellular ␤-galactocerebrosidase activity is an established procedure to diagnose Krabbe disease and monitor the efficacy of gene/stem cell-based therapeutic approaches aimed at restoring defective enzymatic activity in patients or disease models. Current biochemical assays for ␤-galactocerebrosidase show high specificity but generally require large protein amounts from scanty sources such as hematopoietic or neural stem cells. We developed a novel assay based on the hypothesis that specific measurements of ␤-galactocerebrosidase activity can be performed following complete inhibition of ␤-galactosidase activity.