Collaborative Randomized Beamforming for Phased Array Radio Interferometers

The Square Kilometre Array (SKA) will form the largest radio telescope ever built and such a huge instrument in the desert poses enormous engineering and logistic challenges. Algorithmic and architectural breakthroughs are needed. Data is collected and processed in groups of antennas before transport for central processing. This processing includes beamforming, primarily so as to reduce the amount of data sent. The principal existing technique points to a region of interest independently of the sky model and how the other stations beamform. We propose a new collaborative beamforming algorithm in order to maximize information captured at the stations (thus reducing the amount of data transported). The method increases the diversity in measurements through randomized beam- forming. We demonstrate through numerical simulation the effectiveness of the method. In particular, we show that randomized beamforming can achieve the same image quality while producing 40% less data when compared to the prevailing method matched beamforming.Comment: 9 pages, 8 figure

{STAT}3 Interactors as Potential Therapeutic Targets for Cancer Treatment

Signal transducers and activators of transcription (STATs) mediate essential signaling pathways in different biological processes, including immune responses, hematopoiesis, and neurogenesis. Among the STAT members, STAT3 plays crucial roles in cell proliferation, survival, and differentiation. While STAT3 activation is transient in physiological conditions, STAT3 becomes persistently activated in a high percentage of solid and hematopoietic malignancies (e.g., melanoma, multiple myeloma, breast, prostate, ovarian, and colon cancers), thus contributing to malignant transformation and progression. This makes STAT3 an attractive therapeutic target for cancers. Initial strategies aimed at inhibiting STAT3 functions have focused on blocking the action of its activating kinases or sequestering its DNA binding ability. More recently, the diffusion of proteomic-based techniques, which have allowed for the identification and characterization of novel STAT3-interacting proteins able to modulate STAT3 activity via its subcellular localization, interact with upstream kinases, and recruit transcriptional machinery, has raised the possibility to target such cofactors to specifically restrain STAT3 oncogenic functions. In this article, we summarize the available data about the function of STAT3 interactors in malignant cells and discuss their role as potential therapeutic targets for cancer treatment

Acetyl-cholinesterase-inhibitors slow cognitive decline and decrease overall mortality in older patients with dementia

We evaluated the effect of Acetyl-cholinesterase-inhibitors (AChEIs) on cognitive decline and overall survival in a large sample of older patients with late onset Alzheimer's disease (LOAD), vascular dementia (VD) or Lewy body disease (LBD) from a real world setting. Patients with dementia enrolled between 2005 and 2020 by the "Alzheimer's Disease Research Centers" were analysed; the mean follow-up period was 7.9 years. A 1:1 propensity score matching was performed generating a cohort of 1.572 patients (786 treated [AChEIs +] and 786 not treated [AChEIs-] with AChEIs. The MMSE score was almost stable during the first 6 years of follow up in AChEIs + and then declined, while in AChEIs- it progressively declined so that at the end of follow-up (13.6 years) the average decrease in MMSE was 10.8 points in AChEIs- compared with 5.4 points in AChEIs + (p < 0.001). This trend was driven by LOAD (Delta-MMSE:-10.8 vs. -5.7 points; p < 0.001), although a similar effect was observed in VD (Delta-MMSE:-11.6 vs. -8.8; p < 0.001). No effect on cognitive status was found in LBD. At multivariate Cox regression analysis (adjusted for age, gender, dependency level and depression) a strong association between AChEIs therapy and lower all-cause mortality was observed (H.R.:0.59; 95%CI: 0.53-0.66); this was confirmed also in analyses separately conducted in LOAD, VD and LBD. Among older people with dementia, treatment with AChEIs was associated with a slower cognitive decline and with reduced mortality, after a mean follow-up of almost eight years. Our data support the effectiveness of AChEIs in older patients affected by these types of dementia

Tree-ring growth and stable isotopes (13C and 15N) detect effects of wildfires on tree physiological processes in Pinus sylvestris L

Forest fires may alter the physiological and growth processes of trees by causing stress in trees and modifying the availability of soil nutrient. We investigated if, after a high-severity fire, changes in tree-ring growth can be observed, as well as changes in the nitrogen and carbon isotope composition of tree rings of surviving trees. Two wildfires that occurred in Pinus sylvestris L. stands in Northern Italy, one at the beginning and one at the end of the vegetative season, were chosen as the focus of this study. After the fires, the surviving trees showed growth suppression with very narrow tree rings or locally absent rings. The carbon isotope ratio was more negative in tree rings formed in the 5years following fire, indicating better water supply in a situation of less competition. The nitrogen isotope ratio followed opposite trends in the two wildfire stands. In trees cored in the stand where the fire happened at the beginning of the vegetative season, there was no change in the nitrogen isotope ratio, whereas in samples collected in the other fire site, higher nitrogen isotope ratios were observed in the tree rings formed after the fire, reflecting changes in the soil nitrogen supply. Modifications in the growth and isotope composition of the fire-stressed trees disappeared from 6 to 10years after the fire. By studying trees before and after fire, we were able to show that fire affects not only the growth of surviving trees, but also their physiological processe

High-performance deep spiking neural networks with 0.3 spikes per neuron

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks (SNNs) than artificial neural networks (ANNs). This is puzzling given that theoretical results provide exact mapping algorithms from ANNs to SNNs with time-to-first-spike (TTFS) coding. In this paper we analyze in theory and simulation the learning dynamics of TTFS-networks and identify a specific instance of the vanishing-or-exploding gradient problem. While two choices of SNN mappings solve this problem at initialization, only the one with a constant slope of the neuron membrane potential at threshold guarantees the equivalence of the training trajectory between SNNs and ANNs with rectified linear units. We demonstrate that training deep SNN models achieves the exact same performance as that of ANNs, surpassing previous SNNs on image classification datasets such as MNIST/Fashion-MNIST, CIFAR10/CIFAR100 and PLACES365. Our SNN accomplishes high-performance classification with less than 0.3 spikes per neuron, lending itself for an energy-efficient implementation. We show that fine-tuning SNNs with our robust gradient descent algorithm enables their optimization for hardware implementations with low latency and resilience to noise and quantization