The role of HMGA1 protein in gastroenteropancreatic neuroendocrine tumors

Neuroendocrine tumors (NETs) are neoplasms derived from neuroendocrine cells. One of their main features is to often remain asymptomatic and clinically undetectable. High Mobility Group A (HMGA) proteins belong to a family of non-histone chromatinic proteins able to modulate gene expression through the interaction with DNA and transcription factors. They are overexpressed in most of the human malignancies, playing a critical role in carcinogenesis. However, their expression levels and their role in neuroendocrine carcinogenesis has not been exhaustively evaluated until now. Therefore, in this study, we have addressed the validity of using the expression of HMGA1 as a diagnostic marker and have investigated its role in NET carcinogenesis. The expression of HMGA1 has been evaluated by qRT-PCR and immunohistochemistry, using NET tissue microarrays, in a cohort of gastroenteropancreatic (GEP)-NET samples. The expression levels of HMGA1 have been then correlated with the main clinical features of NET samples. Finally, the contribution of HMGA1 overexpression to NET development has been addressed as far as the modulation of proliferation and migration abilities of NET cells is concerned. Here, we report that HMGA1 is overexpressed in GEP-NET samples, at both mRNA and protein levels, and that the silencing of HMGA1 protein expression interferes with the ability of NET cells to proliferate and migrate through the downregulation of Cyclin E, Cyclin B1 and EZH2. These results propose the HMGA proteins as new diagnostic and prognostic markers

Reducing Training Time of Deep Learning Based Digital Backpropagation by Stacking

A method for reducing the training time of a deep learning based digital backpropagation (DL-DBP) is presented. The method is based on dividing a link into smaller sections. A smaller section is then compensated by the DL-DBP algorithm and the same trained model is then reapplied to the subsequent sections. We show in a 32 GBd 16QAM 2400 km 5-channel wavelength division multiplexing transmission link experiment that the proposed stacked DL-DBPs provides a 0.41 dB gain with respect to linear compensation scheme. This needs to be compared with a 0.56 dB gain achieved by a non-stacked DL-DBPs compensated scheme for the price of a 203% increase in total training time. Furthermore, it is shown that by only training the last section of the stacked DL-DBP, one can increase the compensation performance to 0.48 dB.ISSN:1041-1135ISSN:1941-017

Deep Learning Based Digital Back Propagation with Polarization State Rotation & Phase Noise Invariance

A new deep learning training method for digital back propagation (DBP) is introduced. It is invariant to polarization state rotation and phase noise. Applying the method one gains more than 1 dB over standard DBP

Compact Optical TX and RX Macros for Computercom Monolithically Integrated in 45nm CMOS

As the reach of optical communications continues to shrink, photonics is moving from rack-to-rack datacom links to centimeter-scale in-computer applications (computercom) where different architectures are needed. Integrated optical microring resonators (MRRs) are emerging as an attractive choice for fulfilling the more stringent area and efficiency requirements: They offer scaling by wavelength division multiplexing (WDM) and high bandwidth densities. In this paper we present compact electro-optical transmit (TX) and receive (RX) macros for computercom monolithically integrated in 45nm CMOS. They operate with MRR modulators and photodetectors and include all necessary electronics and optics to enable optical links between on-chip data sources and sinks. A most compact implementation for thermal stabilization was enabled by sensing the optical device’s bias currents in the driving electronics instead of using external operating point sensing optics. Using a field-effect transistor as heating element — as is possible in monolithic integration platforms — further reduces area and power necessary for thermal control. The TX macro is shown to work for data rates up to 16 Gb/s with a 5.5 dB extinction ratio (ER) and 2.4 dB insertion loss (IL). The RX macro demonstrates a sensitivity of 71 µApp at 12 Gb/s for a BER ≤ 10-10. An intra-chip link built with the macros achieves ≤ 2.35 pJ/b electrical efficiency and a BER ≤ 10-10 at 10 Gb/s. Both macros are realized within 0.0073 mm2 which amounts to 1.4 Tb/s/mm2 bandwidth density per macro.ISSN:0733-8724ISSN:1558-221

10Gb/s Intra-Chip Compact Electro-Optical Interconnect

We demonstrate a digital-to-optical-to-digital link operating at 10 Gb/s with 2.4 pJ/b below 10-9 BER enabled by zero-change CMOS macros. All necessary electronic-photonic circuits are contained within 0.015 mm2 of silicon area

Deep Learning Based Digital Backpropagation Enabling SNR Gain at Low Complexity

A computationally efficient deep learning based digital backpropagation (DL-DBP) algorithm providing a 1.9 dB SNR over a conventional linear compensation (chromatic dispersion compensation algorithm) and a 1 dB gain over a conventional back-propagation algorithm of the same complexity is presented. The algorithm has been tested in a 1200km transmission experiment. Also, if the algorithm is tested against a conventional digital backpropagation algorithm with the gain, then the new algorithm requires a factor 6 lower complexity. We discuss its training procedure and its principle. We discuss its training procedure and its principle.ISSN:0277-786

Deep learning based digital backpropagation demonstrating SNR gain at low complexity in a 1200 km transmission link

A deep learning (DL) based digital backpropagation (DBP) method with a 1 dB SNR gain over a conventional 1 step per span DBP is demonstrated in a 32 GBd 16QAM transmission across 1200 km. The new DL-DPB is shown to require 6 times less computational power over the conventional DBP scheme. The achievement is possible due to a novel training method in which the DL-DBP is blind to timing error, state of polarization rotation, frequency offset and phase offset. An analysis of the underlying mechanism is given. The applied method first undoes the dispersion, compensates for nonlinear effects in a distributed fashion and reduces the out of band nonlinear modulation due to compensation of the nonlinearities by having a low pass characteristic. We also show that it is sufficient to update the elements of the DL network using a signal with high nonlinearity when dispersion or nonlinearity conditions changes. Lastly, simulation results indicate that the proposed scheme is suitable to deal with impairments from transmission over longer distances. © 2020 Optical Society of America.ISSN:1094-408