Biovalorization of Brewery Waste by Applying Anaerobic Digestion

In the food industry, the brewing sector holds a strategic economic position: in the year 2013, the beer production of the EU-28 was equal to 383,553,000 hL. The brewing process includes chemical and biochemical reactions and solid-liquid separations, involving the generation of various residues and by-products, among which the major two fractions are brewer’s spent grain (BSG), and exhausted brewery yeast (BY). Although until today their main use has been for animal feed, in recent years, several studies have investigated the application of anaerobic digestion in order to revalue the brewery wastes. In this work, specific methane production (SMP) and first-order solubilisation (disintegration+ hydrolysis) rates (ksol) for BSG and BY were evaluated. Biomethanation tests were performed in 5-L fed-batch stirred reactors at several substrate/inoculum ratios. The obtained SMP ranged from 0.255 L CH4 g–1 COD for exhausted brewery yeast to 0.284 L CH4 g–1 COD for brewer’s spent grain. The estimated ksol values ranged from 0.224 d–1 for BSG to 0.659 d–1 for BY

A simple model of radiative emission in M87

We present a simple physical model of the central source emission in the M87 galaxy. It is well known that the observed X-ray luminosity from this galactic nucleus is much lower than the predicted one, if a standard radiative efficiency is assumed. Up to now the main model invoked to explain such a luminosity is the ADAF (Advection-Dominated-Accretion-Flow) model. Our approach supposes only a simple axis-symmetric adiabatic accretion with a low angular momentum together with the bremsstrahlung emission process in the accreting gas. With no other special hypothesis on the dynamics of the system, this model agrees well enough with the luminosity value measured by Chandra.Comment: 11 pages, 6 figures, accepted for publication in The Astrophysical Journa

AMBEATion: Analog Mixed-Signal Back-End Design Automation with Machine Learning and Artificial Intelligence Techniques

For the competitiveness of the European economy, automation techniques in the design of complex electronic systems are a prerequisite for winning the global chip challenge. Specifically, while the physical design of digital Integrated Circuits (ICs) can be largely automated, the physical design of Analog-Mixed-Signal (AMS) ICs built with an analog-on-top flow, where digital subsystems are instantiated as Intellectual Property (IP) modules, is still carried out predominantly by hand, with a time-consuming methodology. The AMBEATion consortium, including global semiconductor and design automation companies as well as leading universities, aims to address this challenge by combining classic Electronic Design Automation (EDA) algorithms with novel Artificial Intelligence and Machine Learning (ML) techniques. Specifically, the scientific and technical result expected at the end of the project will be a new methodology, implemented in a framework of scripts for AMS placement, internally making use of state-of-the-art AI/ML models, and fully integrated with Industrial design flows. With this methodology, the AMBEATion consortium aims to reduce the design turnaround-time and, consequently, the silicon development costs of complex AMS ICs

Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles

BACKGROUND: The risk profile for posterior fossa ependymoma (EP) depends on surgical and molecular status [Group A (PFA) versus Group B (PFB)]. While subtotal tumor resection is known to confer worse prognosis, MRI-based EP risk-profiling is unexplored. We aimed to apply machine learning strategies to link MRI-based biomarkers of high-risk EP and also to distinguish PFA from PFB. METHODS: We extracted 1800 quantitative features from presurgical T2-weighted (T2-MRI) and gadolinium-enhanced T1-weighted (T1-MRI) imaging of 157 EP patients. We implemented nested cross-validation to identify features for risk score calculations and apply a Cox model for survival analysis. We conducted additional feature selection for PFA versus PFB and examined performance across three candidate classifiers. RESULTS: For all EP patients with GTR, we identified four T2-MRI-based features and stratified patients into high- and low-risk groups, with 5-year overall survival rates of 62% and 100%, respectively (p < 0.0001). Among presumed PFA patients with GTR, four T1-MRI and five T2-MRI features predicted divergence of high- and low-risk groups, with 5-year overall survival rates of 62.7% and 96.7%, respectively (p = 0.002). T1-MRI-based features showed the best performance distinguishing PFA from PFB with an AUC of 0.86. CONCLUSIONS: We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy

PI3K/mTOR is a therapeutically targetable genetic dependency in diffuse intrinsic pontine glioma

Diffuse midline glioma (DMG), including tumors diagnosed in the brainstem (diffuse intrinsic pontine glioma; DIPG), are uniformly fatal brain tumors that lack effective treatment. Analysis of CRISPR/Cas9 loss-of-function gene deletion screens identified PIK3CA and MTOR as targetable molecular dependencies across patient derived models of DIPG, highlighting the therapeutic potential of the blood-brain barrier–penetrant PI3K/Akt/mTOR inhibitor, paxalisib. At the human-equivalent maximum tolerated dose, mice treated with paxalisib experienced systemic glucose feedback and increased insulin levels commensurate with patients using PI3K inhibitors. To exploit genetic dependence and overcome resistance while maintaining compliance and therapeutic benefit, we combined paxalisib with the antihyperglycemic drug metformin. Metformin restored glucose homeostasis and decreased phosphorylation of the insulin receptor in vivo, a common mechanism of PI3K-inhibitor resistance, extending survival of orthotopic models. DIPG models treated with paxalisib increased calcium-activated PKC signaling. The brain penetrant PKC inhibitor enzastaurin, in combination with paxalisib, synergistically extended the survival of multiple orthotopic patient-derived and immunocompetent syngeneic allograft models; benefits potentiated in combination with metformin and standard-of-care radiotherapy. Therapeutic adaptation was assessed using spatial transcriptomics and ATAC-Seq, identifying changes in myelination and tumor immune microenvironment crosstalk. Collectively, this study has identified what we believe to be a clinically relevant DIPG therapeutic combinational strategy