Abstract: A filling valve group for a filling machine, comprising a supply duct for delivering a liquid into a container, an air return tube, and a shutter provided with a narrowing portion fixed to the supply duct and an enlarged portion fixed to the air return tube. In addition, the filling valve group comprises a centering body, which is placed at the outlet section of the supply duct to abuttingly receive the mouth of the container, and is removably fixed to a lower portion of the filling valve group by a first quick coupling. The filling valve group further comprises a fixed assembly, which forms an upper section of the supply duct, and a removable tube, which forms a lower section of the supply duct, carries fixed thereto the narrowing portion of the shutter and is removably fixed to the fixed assembly by a second quick coupling.

Abstract: The present invention relates to a peptide having the sequence from N-terminus to C-terminus X-2X-1ZX1X2X3X4X5 wherein Z is tyrosine, phosphotyrosine or a non-natural analogue of phosphotyrosine, such as phosphonodifluoromethyl phenylalanine (F2Pmp) X-2 is a hydrophobic amino acid, such as Leu, Ile, Val, Phe, Tyr, Trp and Met X-1 is any amino acid X1 is a hydrophobic amino acid, such as Ile, Leu, Val, Phe, Tyr, Trp and Met X3 is a hydrophobic amino acid, such as Leu, Ile, Val, Phe, Tyr, Trp and Met X5 is a hydrophobic amino acid, such as Trp, Ile, Val, Phe, Tyr, and Met X2 and X4 are anionic amino acids, preferably each independently is Asp or Glu. The peptide inhibits protein-protein interactions of the Src homology 2 domain-containing phosphatase 2 (SHP2), for the treatment of cancer and RASopathies and as a biomedical research tool.

Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving a request to monitor for anomalies from one or more data sources; analyzing time-series data from the one or more data sources; generating a recommendation for handling the determined anomaly, the recommendation generated by performing one or more of a root cause analysis, a heuristic analysis, and an incident similarity analysis; and reporting the anomaly and recommendation.

Abstract: A composite time series forecasting model comprising a neural network sub-model and one or more state space sub-models corresponding to individual time series is trained. During training, output of the neural network sub-model is used to determine parameters of the state space sub-models, and a loss function is computed using the values of the time series and probabilistic values generated as output by the state space sub-models. A trained version of the composite model is stored.

Abstract: Techniques for anomaly detection are described.