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Previously, we reported from the Sulfolobus solfataricus open reading frame (ORF) SSO2517 the cloning, overexpression and characterization of an esterase belonging to the hormone-sensitive lipase (HSL) family and apparently having a deletion at the N-terminus, which we named SsoNΔ. Searching the recently reported Sulfolobus acidocaldarius genome by sequence alignment, using SSO2517 as a query, allowed identity of a putative esterase (ORF SAC1105) sharing high sequence similarity (82%) with SSO2517. This esterase displays an N-terminus and total length similar to other known esterases of the HSL family. Analysis of the upstream DNA sequence of SS02517 revealed the possibility of expressing a longer version of the protein with an extended N-terminus; however, no clear translation signal consistent with a longer protein version was detected. This new version of SSO2517 was cloned, over-expressed, purified and characterized. The resulting protein, named SsoNΔlong, was 15-fold more active with the substrate p-nitrophenyl hexanoate than SsoNΔ. Furthermore, SsoNΔlong and SsoNΔ displayed different substrate specificities for triacylglycerols. These results and the phylogenetic relationship between S. solfataricus and S. acidocaldarius suggest a common origin of SSO2517 and SAC1105 from an ancestral gene, followed by divergent evolution. Alternatively, a yet-to-be discovered mechanism of translation that directs the expression of SsoNΔlong under specific metabolic conditions could be hypothesized

Evaluation of Endoglin (CD105) expression in pediatric rhabdomyosarcoma

BACKGROUND: The Intratumoral Microvessel Density (IMVD) is commonly used to quantify tumoral vascularization and is usually assessed by pan-endothelial markers, such as CD31. Endoglin (CD105) is a protein predominantly expressed in proliferating endothelium and the IMVD determined by this marker measures specifically the neovascularization. In this study, we investigated the CD105 expression in pediatric rhabdomyosarcoma and assessed the neovascularization by using the angiogenic ratio IMVD-CD105 to IMVD-CD31. METHODS: Paraffin-embedded archival tumor specimens were selected from 65 pediatric patients affected by rhabdomyosarcoma. The expression levels of CD105, CD31 and Vascular Endothelial Growth Factor (VEGF) were investigated in 30 cases (18 embryonal and 12 alveolar) available for this study. The IMVD-CD105 to IMVD-CD31 expression ratio was correlated with clinical and pathologic features of these patients. RESULTS: We found a specific expression of endoglin (CD105) in endothelial cells of all the rhabdomyosarcoma specimens analyzed. We observed a significant positive correlation between the IMVD individually measured by CD105 and CD31. The CD105/CD31 expression ratio was significantly higher in patients with lower survival and embryonal histology. Indeed, patients with a CD105/CD31 expression ratio < 1.3 had a significantly increased OS (88%, 95%CI, 60%-97%) compared to patients with higher values (40%, 95%CI, 12%-67%). We did not find any statistical correlation among VEGF and EFS, OS and CD105/CD31 expression ratio. CONCLUSION: CD105 is expressed on endothelial cells of rhabdomyosarcoma and represent a useful tool to quantify neovascularization in this tumor. If confirmed by further studies, these results will indicate that CD105 is a potential target for combined therapies in rhabdomyosarcoma.We thank Professor Franco Locatelli for critical reading this paper and for his suggestions. We would also like to thank the children ’ s parents, who gave their informed consent for publication and “Il cuore grande di Flavio ”Onlus. Dr. Marta Colletti is a post-doctoral fellow of the Umberto Veronesi Founda- tion. To Valentina Polcini for proofreading.S

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science - the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs

CERN PROTON IRRADIATION FACILITY (IRRAD) DATA MANAGEMENT, CONTROL AND MONITORING SYSTEM INFRASTRUCTURE FOR POST-LS2 EXPERIMENTS

Since upgrades of the CERN Large Hadron Collider are planned and design studies for a post-LHC particle accelerator are ongoing, it is key to ensure that the detectors and electronic components used in the CERN experiments and accelerators can withstand the high amount of radiation produced during particle collisions. To comply with this requirement, scientists perform radiation testing experiments, which consist in exposing these components to high levels of particle radiation to simulate the real operational conditions.The CERN Proton Irradiation Facility (IRRAD) is a well-established reference facility for conducting such experiments. Over the years, the IRRAD facility has developed a dedicated software infrastructure to support the control andmonitoring systems used to manage these experiments, as well as to handle other important aspects such as dosimetry, spectrometry, and material traceability. In this paper, new developments and upgrades to the IRRAD software infrastructure are presented. These advances are crucial to ensure that the facility remains up-to-date and able to cope with the increasing (and always more complex) user needs. These software upgrades (some of them carried out within the EUfunded project AIDAinnova and EURO-LABS) will help to improve the efficiency and accuracy of the experiments performed at IRRAD and enhance the capabilities of this facility

CERN PROTON IRRADIATION FACILITY (IRRAD) DATA MANAGEMENT, CONTROL AND MONITORING SYSTEM INFRASTRUCTURE FOR POST-LS2 EXPERIMENTS

Since upgrades of the CERN Large Hadron Collider are planned and design studies for a post-LHC particle accelerator are ongoing, it is key to ensure that the detectors and electronic components used in the CERN experiments and accelerators can withstand the high amount of radiation produced during particle collisions. To comply with this requirement, scientists perform radiation testing experiments, which consist in exposing these components to high levels of particle radiation to simulate the real operational conditions. The CERN Proton Irradiation Facility (IRRAD) is a well-established reference facility for conducting such experiments. Over the years, the IRRAD facility has developed a dedicated software infrastructure to support the control and monitoring systems used to manage these experiments, as well as to handle other important aspects such as dosimetry, spectrometry, and material traceability. In this paper, new developments and upgrades to the IRRAD software infrastructure are presented. These advances are crucial to ensure that the facility remains up-to-date and able to cope with the increasing (and always more complex) user needs. These software upgrades (some of them carried out within the EUfunded project AIDAinnova and EURO-LABS) will help to improve the efficiency and accuracy of the experiments performed at IRRAD and enhance the capabilities of this facility

The IRRAD Data Manager (IDM)

International audienc