Long-Term Simulations of Beam-Beam Dynamics on GPUs

Future machines such as the electron-ion colliders (JLEIC), linac-ring machines (eRHIC) or LHeC are particularly sensitive to beam-beam effects. This is the limiting factor for long-term stability and high luminosity reach. The complexity of the non-linear dynamics makes it challenging to perform such simulations which require millions of turns. Until recently, most of the methods used linear approximations and/or tracking for a limited number of turns. We have developed a framework which exploits a massively parallel Graphical Processing Units (GPU) architecture to allow for tracking millions of turns in a sympletic way up to an arbitrary order and colliding them at each turn. The code is called GHOST for GPU-accelerated High-Order Symplectic Tracking. As of now, there is no other code in existence that can accurately model the single-particle non-linear dynamics and the beam-beam effect at the same time for a large enough number of turns required to verify the long-term stability of a collider. Our approach relies on a matrix-based arbitrary-order symplectic particle tracking for beam transport and the Bassetti-Erskine approximation for the beam-beam interaction

Prototype tasks: Improving crowdsourcing results through rapid, iterative task design

Low-quality results have been a long-standing problem on microtask crowdsourcing platforms, driving away requesters and justifying low wages for workers. To date, workers have been blamed for low-quality results: they are said to make as little effort as possible, do not pay attention to detail, and lack expertise. In this paper, we hypothesize that requesters may also be responsible for low-quality work: they launch unclear task designs that confuse even earnest workers, under-specify edge cases, and neglect to include examples. We introduce prototype tasks, a crowdsourcing strategy requiring all new task designs to launch a small number of sample tasks. Workers attempt these tasks and leave feedback, enabling the requester to iterate on the design before publishing it. We report a field experiment in which tasks that underwent prototype task iteration produced higher-quality work results than the original task designs. With this research, we suggest that a simple and rapid iteration cycle can improve crowd work, and we provide empirical evidence that requester “quality” directly impacts result quality

The Daemo crowdsourcing marketplace

The success of crowdsourcing markets is dependent on a strong foundation of trust between workers and requesters. In current marketplaces, workers and requesters are often unable to trust each other’s quality, and their mental models of tasks are misaligned due to ambiguous instructions or confusing edge cases. This breakdown of trust typically arises from (1) flawed reputation systems which do not accurately reflect worker and requester quality, and from (2) poorly designed tasks. In this demo, we present how Boomerang and Prototype Tasks, the fundamental building blocks of the Daemo crowdsourcing marketplace, help restore trust between workers and requesters. Daemo’s Boomerang reputation system incentivizes alignment between opinion and ratings by determining the likelihood that workers and requesters will work together in the future based on how they rate each other. Daemo’s Prototype tasks require that new tasks go through a feedback iteration phase with a small number of workers so that requesters can revise their instructions and task designs before launch

Dimerization of Receptor Protein-Tyrosine Phosphatase alpha in living cells

BACKGROUND: Dimerization is an important regulatory mechanism of single membrane-spanning receptors. For instance, activation of receptor protein-tyrosine kinases (RPTKs) involves dimerization. Structural, functional and biochemical studies suggested that the enzymatic counterparts of RPTKs, the receptor protein-tyrosine phosphatases (RPTPs), are inhibited by dimerization, but whether RPTPs actually dimerize in living cells remained to be determined. RESULTS: In order to assess RPTP dimerization, we have assayed Fluorescence Resonance Energy Transfer (FRET) between chimeric proteins of cyan- and yellow-emitting derivatives of green fluorescent protein, fused to RPTPα, using three different techniques: dual wavelength excitation, spectral imaging and fluorescence lifetime imaging. All three techniques suggested that FRET occurred between RPTPα -CFP and -YFP fusion proteins, and thus that RPTPα dimerized in living cells. RPTPα dimerization was constitutive, extensive and specific. RPTPα dimerization was consistent with cross-linking experiments, using a non-cell-permeable chemical cross-linker. Using a panel of deletion mutants, we found that the transmembrane domain was required and sufficient for dimerization. CONCLUSIONS: We demonstrate here that RPTPα dimerized constitutively in living cells, which may be mediated by the transmembrane domain, providing strong support for the model that dimerization is involved in regulation of RPTPs

Boomerang: Rebounding the consequences of reputation feedback on crowdsourcing platforms

Paid crowdsourcing platforms suffer from low-quality workand unfair rejections, but paradoxically, most workers and requesters have high reputation scores. These inflated scores, which make high-quality work and workers difficult to find,stem from social pressure to avoid giving negative feedback. We introduce Boomerang, a reputation system for crowdsourcing that elicits more accurate feedback by rebounding the consequences of feedback directly back onto the person who gave it. With Boomerang, requesters find that their highly rated workers gain earliest access to their future tasks, and workers find tasks from their highly-rated requesters at the top of their task feed. Field experiments verify that Boomerang causes both workers and requesters to provide feedback that is more closely aligned with their private opinions. Inspired by a game-theoretic notion of incentive-compatibility, Boomerang opens opportunities for interaction design to incentivize honest reporting over strategic dishonesty

Crowd guilds: Worker-led reputation and feedback on crowdsourcing platforms

Crowd workers are distributed and decentralized. While decentralization is designed to utilize independent judgment to promote high-quality results, it paradoxically undercuts behaviors and institutions that are critical to high-quality work. Reputation is one central example: crowdsourcing systems depend on reputation scores from decentralized workers and requesters, but these scores are notoriously inflated and uninformative. In this paper, we draw inspiration from historical worker guilds (e.g., in the silk trade) to design and implement crowd guilds: centralized groups of crowd workers who collectively certify each other’s quality through double-blind peer assessment. A two-week field experiment compared crowd guilds to a traditional decentralized crowd work model. Crowd guilds produced reputation signals more strongly correlated with ground-truth worker quality than signals available on current crowd working platforms, and more accurate than in the traditional model

Autocrine Activation of the MET Receptor Tyrosine Kinase in Acute Myeloid Leukemia

Although the treatment of acute myeloid leukemia (AML) has improved significantly, more than half of all patients develop disease that is refractory to intensive chemotherapy. Functional genomics approaches offer a means to discover specific molecules mediating aberrant growth and survival of cancer cells. Thus, using a loss-of-function RNA interference genomic screen, we identified aberrant expression of the hepatocyte growth factor (HGF) as a critical factor in AML pathogenesis. We found HGF expression leading to autocrine activation of its receptor tyrosine kinase, MET, in nearly half of the AML cell lines and clinical samples studied. Genetic depletion of HGF or MET potently inhibited the growth and survival of HGF-expressing AML cells. However, leukemic cells treated with the specific MET kinase inhibitor crizotinib developed resistance due to compensatory upregulation of HGF expression, leading to restoration of MET signaling. In cases of AML where MET is coactivated with other tyrosine kinases, such as fibroblast growth factor receptor 1 (FGFR1), concomitant inhibition of FGFR1 and MET blocked compensatory HGF upregulation, resulting in sustained logarithmic cell kill both in vitro and in xenograft models in vivo. Our results demonstrate widespread dependence of AML cells on autocrine activation of MET, as well as the importance of compensatory upregulation of HGF expression in maintaining leukemogenic signaling by this receptor. We anticipate that these findings will lead to the design of additional strategies to block adaptive cellular responses that drive compensatory ligand expression as an essential component of the targeted inhibition of oncogenic receptors in human cancers

Dysregulated lipid synthesis by oncogenic IDH1 mutation is a targetable synthetic lethal vulnerability

Isocitrate dehydrogenase 1 and 2 (IDH) are mutated in multiple cancers and drive production of (R)-2-hydroxyglutarate (2HG). We identified a lipid synthesis enzyme (acetyl CoA carboxylase 1, ACC1) as a synthetic lethal target in mutant IDH1 (mIDH1), but not mIDH2, cancers. Here, we analyzed the metabolome of primary acute myeloid leukemia (AML) blasts and identified a mIDH1-specific reduction in fatty acids. mIDH1 also induced a switch to beta-oxidation indicating reprogramming of metabolism towards a reliance on fatty acids. Compared to mIDH2, mIDH1 AML displayed depletion of NADPH with defective reductive carboxylation that was not rescued by the mIDH1-specific inhibitor ivosidenib. In xenograft models, a lipid-free diet markedly slowed the growth of mIDH1 AML, but not healthy CD34+ HSPCs or mIDH2 AML. Genetic and pharmacologic targeting of ACC1 resulted in growth inhibition of mIDH1 cancers, not reversible by ivosidenib. Critically, pharmacologic targeting of ACC1 improved sensitivity of mIDH1 AML to venetoclax.Daniel Thomas, Manhong Wu, Yusuke Nakauchi, Ming Zheng, Chloe A.L. Thompson-Peach, Kelly Lim, Niklas Landberg, Thomas Köhnke, Nirmal Robinson, Satinder Kaur, Monika Kutyna, Melissa Stafford, Devendra Hiwase, Andreas Reinisch, Gary Peltz, Ravindra Majet