A 3D track finder for the Belle II CDC L1 trigger

Machine learning methods are integrated into the pipelined first level (L1) track trigger of the upgraded flavor physics experiment Belle II at KEK in Tsukuba, Japan. The novel triggering techniques cope with the severe background from events outside the small collision region provided by the new SuperKEKB asymmetric-energy electron-positron collider. Using the precise drift-time information of the central drift chamber which provides axial and stereo wire layers, a neural network L1 trigger estimates the 3D track parameters of tracks, based on input from the axial wire planes provided by a 2D track finder. An extension of this 2D Hough track finder to a 3D finder is proposed, where the single hit representations in the Hough plane are trained using Monte Carlo. This 3D finder improves the track finding efficiency by including the stereo sense wires as input. The estimated polar track angle allows a specialization of the subsequent neural networks to sectors in the polar angle

STAT3 regulated ARF expression suppresses prostate cancer metastasis.

Prostate cancer (PCa) is the most prevalent cancer in men. Hyperactive STAT3 is thought to be oncogenic in PCa. However, targeting of the IL-6/STAT3 axis in PCa patients has failed to provide therapeutic benefit. Here we show that genetic inactivation of Stat3 or IL-6 signalling in a Pten-deficient PCa mouse model accelerates cancer progression leading to metastasis. Mechanistically, we identify p19(ARF) as a direct Stat3 target. Loss of Stat3 signalling disrupts the ARF-Mdm2-p53 tumour suppressor axis bypassing senescence. Strikingly, we also identify STAT3 and CDKN2A mutations in primary human PCa. STAT3 and CDKN2A deletions co-occurred with high frequency in PCa metastases. In accordance, loss of STAT3 and p14(ARF) expression in patient tumours correlates with increased risk of disease recurrence and metastatic PCa. Thus, STAT3 and ARF may be prognostic markers to stratify high from low risk PCa patients. Our findings challenge the current discussion on therapeutic benefit or risk of IL-6/STAT3 inhibition.Lukas Kenner and Jan Pencik are supported by FWF, P26011 and the Genome Research-Austria project “Inflammobiota” grants. Helmut Dolznig is supported by the Herzfelder Family Foundation and the Niederösterr. Forschungs-und Bildungsges.m.b.H (nfb). Richard Moriggl is supported by grant SFB-F2807 and SFB-F4707 from the Austrian Science Fund (FWF), Ali Moazzami is supported by Infrastructure for biosciences-Strategic fund, SciLifeLab and Formas, Zoran Culig is supported by FWF, P24428, Athena Chalaris and Stefan Rose-John are supported by the Deutsche Forschungsgemeinschaft (Grant SFB 877, Project A1and the Cluster of Excellence --“Inflammation at Interfaces”). Work of the Aberger lab was supported by the Austrian Science Fund FWF (Projects P25629 and W1213), the European FP7 Marie-Curie Initial Training Network HEALING and the priority program Biosciences and Health of the Paris-Lodron University of Salzburg. Valeria Poli is supported by the Italian Association for Cancer Research (AIRC, No IG13009). Richard Kennedy and Steven Walker are supported by the McClay Foundation and the Movember Centre of Excellence (PC-UK and Movember). Gerda Egger is supported by FWF, P27616. Tim Malcolm and Suzanne Turner are supported by Leukaemia and Lymphoma Research.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms873

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

The Concise Guide to PHARMACOLOGY 2023/24: G protein-coupled receptors.

peer reviewedThe Concise Guide to PHARMACOLOGY 2023/24 is the sixth in this series of biennial publications. The Concise Guide provides concise overviews, mostly in tabular format, of the key properties of approximately 1800 drug targets, and about 6000 interactions with about 3900 ligands. There is an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (https://www.guidetopharmacology.org), which provides more detailed views of target and ligand properties. Although the Concise Guide constitutes almost 500 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point-in-time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/bph.16177. G protein-coupled receptors are one of the six major pharmacological targets into which the Guide is divided, with the others being: ion channels, nuclear hormone receptors, catalytic receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid-2023, and supersedes data presented in the 2021/22, 2019/20, 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate

Endoplasmic reticulum stress in the intestinal epithelium initiates purine metabolite synthesis and promotes Th17 cell differentiation in the gut.

Intestinal IL-17-producing T helper (Th17) cells are dependent on adherent microbes in the gut for their development. However, how microbial adherence to intestinal epithelial cells (IECs) promotes Th17 cell differentiation remains enigmatic. Here, we found that Th17 cell-inducing gut bacteria generated an unfolded protein response (UPR) in IECs. Furthermore, subtilase cytotoxin expression or genetic removal of X-box binding protein 1 (Xbp1) in IECs caused a UPR and increased Th17 cells, even in antibiotic-treated or germ-free conditions. Mechanistically, UPR activation in IECs enhanced their production of both reactive oxygen species (ROS) and purine metabolites. Treating mice with N-acetyl-cysteine or allopurinol to reduce ROS production and xanthine, respectively, decreased Th17 cells that were associated with an elevated UPR. Th17-related genes also correlated with ER stress and the UPR in humans with inflammatory bowel disease. Overall, we identify a mechanism of intestinal Th17 cell differentiation that emerges from an IEC-associated UPR

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

A Dual Role of Caspase-8 in Triggering and Sensing Proliferation-Associated DNA Damage, a Key Determinant of Liver Cancer Development

Concomitant hepatocyte apoptosis and regeneration is a hallmark of chronic liver diseases (CLDs) predisposing to hepatocellular carcinoma (HCC). Here, we mechanistically link caspase-8-dependent apoptosis to HCC development via proliferation- and replication-associated DNA damage. Proliferation-associated replication stress, DNA damage, and genetic instability are detectable in CLDs before any neoplastic changes occur. Accumulated levels of hepatocyte apoptosis determine and predict subsequent hepatocarcinogenesis. Proliferation-associated DNA damage is sensed by a complex comprising caspase-8, FADD, c-FLIP, and a kinase-dependent function of RIPK1. This platform requires a non-apoptotic function of caspase-8, but no caspase-3 or caspase-8 cleavage. It may represent a DNA damage-sensing mechanism in hepatocytes that can act via JNK and subsequent phosphorylation of the histone variant H2AX.ISSN:1535-6108ISSN:1878-368