Search

Abstract In an external electric or magnetic field, a gravitational wave (GW) may be converted into electromagnetic radiation. We present a coordinate-invariant framework to describe the GW signal in a detector that is based on this effect, such as cavities for axion searches. In this framework, we pay special attention to the definition of manifestly coordinate-independent expressions for the electromagnetic fields that an external observer would detect. A careful assessment of the detector’s perceived motion allows us to treat both its mechanical and its electromagnetic response to the GW consistently. We further introduce well-defined approximations for which this motion may be neglected, and hence provide suggestions on which coordinate frame is suitable to characterise the GW signal in practice. We illustrate our findings in two examples, an infinitesimally thin rod and a spherical electromagnetic cavity.

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.

Graduate Student Instructors (GSIs) are often framed as resistant to Writing Pedagogy Education (WPE) (Grouling; Hesse; Reed). Yet, these moments of resistance can (and should be) reframed as acts of well-being, where GSIs are establishing boundaries and identifying their own self-care and needs. I draw on the experiences of five different GSIs in a writing program using data from interviews, focus groups, coding sessions, observations, teaching philosophies, and reflections to illustrate how we can rethink the narrative of resistance as well-being to more productively design and implement teacher training. Using Cochran's PREMISE model, I focus on well-being as related to GSIs and how this model maps onto existing WPE structures. At the end of the article, I provide a table for various stakeholders to identify ways to highlight GSI well-being and establish productive disciplinary practices that can reshape what it means to be professionalized in academia and how we can view acts of well-being as radical acts that challenge traditional academic structures at large.

Abstract Transcriptional cofactors of the ETO family are recurrent fusion partners in acute leukemia. We characterized the ETO2 regulome by integrating transcriptomic and chromatin binding analyses in human erythroleukemia xenografts and controlled ETO2 depletion models. We demonstrate that beyond its well‐established repressive activity, ETO2 directly activates transcription of MYB, among other genes. The ETO2‐activated signature is associated with a poorer prognosis in erythroleukemia but also in other acute myeloid and lymphoid leukemia subtypes. Mechanistically, ETO2 colocalizes with EP300 and MYB at enhancers supporting the existence of an ETO2/MYB feedforward transcription activation loop (e.g., on MYB itself). Both small‐molecule and PROTAC‐mediated inhibition of EP300 acetyltransferases strongly reduced ETO2 protein, chromatin binding, and ETO2‐activated transcripts. Taken together, our data show that ETO2 positively enforces a leukemia maintenance program that is mediated in part by the MYB transcription factor and that relies on acetyltransferase cofactors to stabilize ETO2 scaffolding activity.

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings-most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.

Abstract Pharmacologic targeting of chromatin-associated protein complexes has shown significant responses in KMT2A-rearranged (KMT2A-r) acute myeloid leukemia (AML) but resistance frequently develops to single agents. This points to a need for therapeutic combinations that target multiple mechanisms. To enhance our understanding of functional dependencies in KMT2A-r AML, we have used a proteomic approach to identify the catalytic immunoproteasome subunit PSMB8 as a specific vulnerability. Genetic and pharmacologic inactivation of PSMB8 results in impaired proliferation of murine and human leukemic cells while normal hematopoietic cells remain unaffected. Disruption of immunoproteasome function drives an increase in transcription factor BASP1 which in turn represses KMT2A-fusion protein target genes. Pharmacologic targeting of PSMB8 improves efficacy of Menin-inhibitors, synergistically reduces leukemia in human xenografts and shows preserved activity against Menin-inhibitor resistance mutations. This identifies and validates a cell-intrinsic mechanism whereby selective disruption of proteostasis results in altered transcription factor abundance and repression of oncogene-specific transcriptional networks. These data demonstrate that the immunoproteasome is a relevant therapeutic target in AML and that targeting the immunoproteasome in combination with Menin-inhibition could be a novel approach for treatment of KMT2A-r AML.

Touch sensation is primarily encoded by mechanoreceptors, called low-threshold mechanoreceptors (LTMRs), with their cell bodies in the dorsal root ganglia. Because of their great diversity in terms of molecular signature, terminal endings morphology, and electrophysiological properties, mirroring the complexity of tactile experience, LTMRs are a model of choice to study the molecular cues differentially controlling neuronal diversification. While the transcriptional codes that define different LTMR subtypes have been extensively studied, the molecular players that participate in their late maturation and in particular in the striking diversity of their end-organ morphological specialization are largely unknown. Here we identified the TALE homeodomain transcription factor Meis2 as a key regulator of LTMRs target-field innervation in mice. Meis2 is specifically expressed in cutaneous LTMRs, and its expression depends on target-derived signals. While LTMRs lacking Meis2 survived and are normally specified, their end-organ innervations, electrophysiological properties, and transcriptome are differentially and markedly affected, resulting in impaired sensory-evoked behavioral responses. These data establish Meis2 as a major transcriptional regulator controlling the orderly formation of sensory neurons innervating peripheral end organs required for light touch.

Abstract We present Clipboard‐to‐SMILES Converter (C2SC), a macOS application that directly converts molecular structures from the clipboard. The app focuses on seamlessly converting screenshots of molecules into a desired molecular representation. It supports a wide range of molecular representations, such as SMILES, SELFIES, InChI's, IUPAC names, RDKit Mol's, and CAS numbers, allowing effortless conversion between these formats within the clipboard. C2SC automatically saves converted molecules to a local history file and displays the last 10 entries for quick access. Additionally, it incorporates several SMILES operations, including canonicalization, augmentation, as well price‐searching molecules on chemical vendors for the cost‐effective purchasing option. Beyond the one‐click conversion from clipboard to molecular structures, the app offers continuous monitoring of the clipboard which automatically converts any supported representations or images detected into SMILES. The convenient interface, directly in the status bar, as well as availability as macOS application, makes C2SC useful for a broad community not requiring any programming expertise. Most conversions are performed locally, notably the image‐to‐SMILES conversion, with internet access only necessary for specific tasks like price lookups. In summary, C2SC provides a user‐friendly and efficient solution for converting molecular structures directly from the clipboard, offering seamless conversions between comprehensive chemical representations and can be directly downloaded from https://github.com/O-Schilter/Clipboard-to-SMILES-Converter.

Summary: Acute myeloid leukemia (AML) progression is influenced by immune suppression induced by leukemia cells. ZEB1, a critical transcription factor in epithelial-to-mesenchymal transition, demonstrates immune regulatory functions in AML. Silencing ZEB1 in leukemic cells reduces engraftment and extramedullary disease in immune-competent mice, activating CD8 T lymphocytes and limiting Th17 cell expansion. ZEB1 in AML cells directly promotes Th17 cell development that, in turn, creates a self-sustaining loop and a pro-invasive phenotype, favoring transforming growth factor β (TGF-β), interleukin-23 (IL-23), and SOCS2 gene transcription. In bone marrow biopsies from AML patients, immunohistochemistry shows a direct correlation between ZEB1 and Th17. Also, the analysis of ZEB1 expression in larger datasets identifies two distinct AML groups, ZEB1high and ZEB1low, each with specific immunological and molecular traits. ZEB1high patients exhibit increased IL-17, SOCS2, and TGF-β pathways and a negative association with overall survival. This unveils ZEB1’s dual role in AML, entwining pro-tumoral and immune regulatory capacities in AML blasts.

Abstract In recent years, several pulsar timing array collaborations have reported first hints for a stochastic gravitational wave background at nano-Hertz frequencies. Here we elaborate on the possibility that this signal comes from new physics that leads to the generation of a primordial stochastic gravitational wave background. We propose a set of simple but concrete models that can serve as benchmarks for gravitational waves sourced by cosmological phase transitions, domain wall networks, cosmic strings, axion dynamics, or large scalar fluctuations. These models are then confronted with pulsar timing data and with cosmological constraints. With only a limited number of free parameters per model, we are able to identify viable regions of parameter space and also make predictions for future astrophysical and laboratory tests that can help with model identification and discrimination.

Abstract The myeloid transcription factor CEBPA is recurrently biallelically mutated (i.e., double mutated; CEBPA DM) in acute myeloid leukemia (AML) with a combination of hypermorphic N-terminal mutations (CEBPA NT), promoting expression of the leukemia-associated p30 isoform, and amorphic C-terminal mutations. The most frequently co-mutated genes in CEBPA DM AML are GATA2 and TET2, however the molecular mechanisms underlying this co-mutational spectrum are incomplete. By combining transcriptomic and epigenomic analyses of CEBPA-TET2 co-mutated patients with models thereof, we identify GATA2 as a conserved target of the CEBPA-TET2 mutational axis, providing a rationale for the mutational spectra in CEBPA DM AML. Elevated CEBPA levels, driven by CEBPA NT, mediate recruitment of TET2 to the Gata2 distal hematopoietic enhancer thereby increasing Gata2 expression. Concurrent loss of TET2 in CEBPA DM AML induces a competitive advantage by increasing Gata2 promoter methylation, thereby rebalancing GATA2 levels. Of clinical relevance, demethylating treatment of Cebpa-Tet2 co-mutated AML restores Gata2 levels and prolongs disease latency.

Abstract The Laser Interferometer Space Antenna (LISA) has two scientific objectives of cosmological focus: to probe the expansion rate of the universe, and to understand stochastic gravitational-wave backgrounds and their implications for early universe and particle physics, from the MeV to the Planck scale. However, the range of potential cosmological applications of gravitational-wave observations extends well beyond these two objectives. This publication presents a summary of the state of the art in LISA cosmology, theory and methods, and identifies new opportunities to use gravitational-wave observations by LISA to probe the universe.

In this work, we consider the case of a strongly coupled dark/hidden sector, which extends the Standard Model (SM) by adding an additional non-Abelian gauge group. These extensions generally contain matter fields, much like the SM quarks, and gauge fields similar to the SM gluons. We focus on the exploration of such sectors where the dark particles are produced at the LHC through a portal and undergo rapid hadronization within the dark sector before decaying back, at least in part and potentially with sizeable lifetimes, to SM particles, giving a range of possibly spectacular signatures such as emerging or semi-visible jets. Other, non-QCD-like scenarios leading to soft unclustered energy patterns or glueballs are also discussed. After a review of the theory, existing benchmarks and constraints, this work addresses how to build consistent benchmarks from the underlying physical parameters and present new developments for the pythia Hidden Valley module, along with jet substructure studies. Finally, a series of improved search strategies is presented in order to pave the way for a better exploration of the dark showers at the LHC.

The Forward Physics Facility (FPF) is a proposal to create a cavern with thespace and infrastructure to support a suite of far-forward experiments at theLarge Hadron Collider during the High Luminosity era. Located along the beamcollision axis and shielded from the interaction point by at least 100 m ofconcrete and rock, the FPF will house experiments that will detect particlesoutside the acceptance of the existing large LHC experiments and will observerare and exotic processes in an extremely low-background environment. In thiswork, we summarize the current status of plans for the FPF, including recentprogress in civil engineering in identifying promising sites for the FPF andthe experiments currently envisioned to realize the FPF's physics potential. Wethen review the many Standard Model and new physics topics that will beadvanced by the FPF, including searches for long-lived particles, probes ofdark matter and dark sectors, high-statistics studies of TeV neutrinos of allthree flavors, aspects of perturbative and non-perturbative QCD, andhigh-energy astroparticle physics.

The vascularized fibula transfer is a well-established technique for extremity reconstruction, but operative planning and patient selection remains crucial. Although recently developed techniques for bone reconstruction, such as bone segment transfer, are becoming increasingly popular, bone defects may still require vascularized bone grafts under certain circumstances. In this study, 41 cases, 28 (68%) men and 13 (32%) women (median age: 40 years), were retrospectively analyzed. Therapy-specific data (flap vascularity [free vs. pedicled] size in cm and configuration [single- vs. double-barrel], mode of fixation [internal/external]) and potential risk factors were ascertained. Indications for reconstruction were osteomyelitis at host site (n = 23, 55%), pseudarthrosis (n = 8, 20%), congenital deformity (n = 6, 15%), traumatic defect, and giant cell tumor of the bone (n = 2, 5% each). Complete healing occurred in 34 (83%) patients after a median time of 6 months. Confounders for prolonged healing were female gender (p = 0.002), reconstruction in the lower limb (p = 0.011), smoking (p = 0.049), and the use of an external fixator (p = 0.009). Six (15%) patients required secondary limb amputation due to reconstruction failure, and one patient had persistent pseudarthrosis at last follow-up. The only risk factor for amputation assessed via logistic regression analysis was preexisting PAOD (peripheral artery occlusive disease; p = 0.008) The free fibula is a reliable tool for extremity reconstruction in various cases, but time to full osseous integration may exceed six months. Patients should be encouraged to cease smoking as it is a modifiable risk factor.

Anhand des Stadtteils Bunker Hill beschrieb Mike Davis Anfang der 1990er-Jahre, wie bewusste sozialräumliche Strategien im Zuge der Reagan-Jahre die Oberfläche von Los Angeles zunehmend verhärteten. Im Herzen des Plattform-Urbanismus im San Francisco der 2020er-Jahre, so die These dieses Beitrags, finden solche Verhärtungen hingegen zunehmend auch unter der Oberfläche statt. Hübsch bespielte pseudo-öffentliche Räume aktivieren Stadtbewohner*innen als Datenproduzent*innen, während sich Plattformfirmen die Kaperung der öffentlichen Infrastruktur auf die Fahne geschrieben haben. Der Festungsarchitektur wird von einer im Hintergrund ablaufenden Dateninfrastruktur der Rang abgelaufen. Die Privatisierung des öffentlichen Raums, die Verdrängung und Kriminalisierung von Unerwünschten und die Gewinnung profitträchtiger Daten aus dem städtischen Alltagsleben laufen dadurch vielleicht subtiler ab. Das Endprodukt sind aber genauso stark polarisierte, fragmentierte und künstlich aufbereitete städtische Räume, die ihren Versprechungen von Urbanität nicht nachkommen. Ein Streifzug zwischen den (zukünftigen) Ruinen des Techbooms in San Francisco, wo gegenwärtige „Ausgrabungen der Zukunft“ besonders ergiebig sind.

The RXN for Chemistry project, initiated by IBM Research Europe – Zurich in 2017, aimed to develop a series of digital assets using machine learning techniques to promote the use of data-driven methodologies in synthetic organic chemistry. This research adopts an innovative concept by treating chemical reaction data as language records, treating the prediction of a synthetic organic chemistry reaction as a translation task between precursor and product languages. Over the years, the IBM Research team has successfully developed language models for various applications including forward reaction prediction, retrosynthesis, reaction classification, atom-mapping, procedure extraction from text, inference of experimental protocols and its use in programming commercial automation hardware to implement an autonomous chemical laboratory. Furthermore, the project has recently incorporated biochemical data in training models for greener and more sustainable chemical reactions. The remarkable ease of constructing prediction models and continually enhancing them through data augmentation with minimal human intervention has led to the widespread adoption of language model technologies, facilitating the digitalization of chemistry in diverse industrial sectors such as pharmaceuticals and chemical manufacturing. This manuscript provides a concise overview of the scientific components that contributed to the prestigious Sandmeyer Award in 2022

Abstract In this work, we consider the case of a strongly coupled dark/hidden sector, which extends the Standard Model (SM) by adding an additional non-Abelian gauge group. These extensions generally contain matter fields, much like the SM quarks, and gauge fields similar to the SM gluons. We focus on the exploration of such sectors where the dark particles are produced at the LHC through a portal and undergo rapid hadronization within the dark sector before decaying back, at least in part and potentially with sizeable lifetimes, to SM particles, giving a range of possibly spectacular signatures such as emerging or semi-visible jets. Other, non-QCD-like scenarios leading to soft unclustered energy patterns or glueballs are also discussed. After a review of the theory, existing benchmarks and constraints, this work addresses how to build consistent benchmarks from the underlying physical parameters and present new developments for the pythia Hidden Valley module, along with jet substructure studies. Finally, a series of improved search strategies is presented in order to pave the way for a better exploration of the dark showers at the LHC.

Summary: Completion of neuronal migration is critical for brain development. Kif21b is a plus-end-directed kinesin motor protein that promotes intracellular transport and controls microtubule dynamics in neurons. Here we report a physiological function of Kif21b during radial migration of projection neurons in the mouse developing cortex. In vivo analysis in mouse and live imaging on cultured slices demonstrate that Kif21b regulates the radial glia-guided locomotion of newborn neurons independently of its motility on microtubules. We show that Kif21b directly binds and regulates the actin cytoskeleton both in vitro and in vivo in migratory neurons. We establish that Kif21b-mediated regulation of actin cytoskeleton dynamics influences branching and nucleokinesis during neuronal locomotion. Altogether, our results reveal atypical roles of Kif21b on the actin cytoskeleton during migration of cortical projection neurons.

Recent studies have suggested that several oncogenic and tumor-suppressive proteins carry out their functions in the context of specific membrane-less cellular compartments. As these compartments, generally referred to as onco-condensates, are specific to tumor cells and are tightly linked to disease development, the mechanisms of their formation and maintenance have been intensively studied. Here we review the proposed leukemogenic and tumor-suppressive activities of nuclear biomolecular condensates in acute myeloid leukemia (AML). We focus on condensates formed by oncogenic fusion proteins including nucleoporin 98 (NUP98), mixed-lineage leukemia 1 (MLL1, also known as KMT2A), mutated nucleophosmin (NPM1c) and others. We also discuss how altered condensate formation contributes to malignant transformation of hematopoietic cells, as described for promyelocytic leukemia protein (PML) in PML::RARA-driven acute promyelocytic leukemia (APL) and other myeloid malignancies. Finally, we discuss potential strategies for interfering with the molecular mechanisms related to AML-associated biomolecular condensates, as well as current limitations of the field.

Inferring missing molecules in chemical equations is an important task in chemistry and drug discovery. In fact, the completion of chemical equations with necessary reagents is important for improving existing datasets by detecting missing compounds, making them compatible with deep learning models that require complete information about reactants, products, and reagents in a chemical equation for increased performance. Here, we present a deep learning model to predict missing molecules using a multi-task approach, which can ultimately be viewed as a generalization of the forward reaction prediction and retrosynthesis models, since both can be expressed in terms of incomplete chemical equations. We illustrate that a single trained model, based on the transformer architecture and acting on reaction SMILES strings, can address the prediction of products (forward), precursors (retro) or any other molecule in arbitrary positions such as solvents, catalysts or reagents (completion). Our aim is to assess whether a unified model trained simultaneously on different tasks can effectively leverage diverse knowledge from various prediction tasks within the chemical domain, compared to models trained individually on each application. The multi-task models demonstrate top-1 performance of 72.4%, 16.1%, and 30.5% for the forward, retro, and completion tasks, respectively. For the same model we computed round-trip accuracy of 83.4%. The completion task exhibiting improvements due to the multi-task approach.

Abstract We propose a search for long lived axion-like particles (ALPs) in exotic top decays. Flavour-violating ALPs appear as low energy effective theories for various new physics scenarios such as t-channel dark sectors or Froggatt-Nielsen models. In this case the top quark may decay to an ALP and an up- or charm-quark. For masses in the few GeV range, the ALP is long lived across most of the viable parameter space, suggesting a dedicated search. We propose to search for these long lived ALPs in t t ¯ $$ t\overline{t} $$ events, using one top quark as a trigger. We focus on ALPs decaying in the hadronic calorimeter, and show that the ratio of energy deposits in the electromagnetic and hadronic calorimeters as well as track vetoes can efficiently suppress Standard Model backgrounds. Our proposed search can probe exotic top branching ratios smaller than 10 −4 with a conservative strategy at the upcoming LHC run, and potentially below the 10 −7 level with more advanced methods. Finally we also show that measurements of single top production probe these branching ratios in the very short and very long lifetime limit at the 10 −3 level.

Reaction optimization is challenging and traditionally delegated to domain experts who iteratively propose increasingly optimal experiments. Problematically, the reaction landscape is complex and often requires hundreds of experiments to reach convergence, representing an enormous resource sink. Bayesian optimization (BO) is an optimization algorithm that recommends the next experiment based on previous observations and has recently gained considerable interest in the general chemistry community. The application of BO for chemical reactions has been demonstrated to increase efficiency in optimization campaigns and can recommend favorable reaction conditions amidst many possibilities. Moreover, its ability to jointly optimize desired objectives such as yield and stereoselectivity makes it an attractive alternative or at least complementary to domain expert-guided optimization. With the democratization of BO software, the barrier of entry to applying BO for chemical reactions has drastically lowered. The intersection between the paradigms will see advancements at an ever-rapid pace. In this review, we discuss how chemical reactions can be transformed into machine-readable formats which can be learned by machine learning (ML) models. We present a foundation for BO and how it has already been applied to optimize chemical reaction outcomes. The important message we convey is that realizing the full potential of ML-augmented reaction optimization will require close collaboration between experimentalists and computational scientists.

This chapter provides an overview of Writing Pedagogy Education and showcases how the University of Arizona puts these opportunities into practice to prepare and professionalize graduate students as writing instructors.

Abstract Background Acute myeloid leukemia (AML) is characterized by accumulation of aberrantly differentiated hematopoietic myeloid progenitor cells. The karyotyping-silent NUP98-NSD1 fusion is a molecular hallmark of pediatric AML and is associated with the activating FLT3-ITD mutation in > 70% of the cases. NUP98-NSD1 fusion protein promotes myeloid progenitor self-renewal in mice via unknown molecular mechanism requiring both the NUP98 and the NSD1 moieties. Methods We used affinity purification coupled to label-free mass spectrometry (AP-MS) to examine the effect of NUP98-NSD1 structural domain deletions on nuclear interactome binding. We determined their functional relevance in NUP98-NSD1 immortalized primary murine hematopoietic stem and progenitor cells (HSPC) by inducible knockdown, pharmacological targeting, methylcellulose assay, RT-qPCR analysis and/or proximity ligation assays (PLA). Fluorescence recovery after photobleaching and b-isoxazole assay were performed to examine the phase transition capacity of NUP98-NSD1 in vitro and in vivo. Results We show that NUP98-NSD1 core interactome binding is largely dependent on the NUP98 phenylalanine-glycine (FG) repeat domains which mediate formation of liquid-like phase-separated NUP98-NSD1 nuclear condensates. We identified condensate constituents including imitation switch (ISWI) family member SMARCA5 and BPTF (bromodomain PHD finger transcription factor), both members of the nucleosome remodeling factor complex (NURF). We validated the interaction with SMARCA5 in NUP98-NSD1+ patient cells and demonstrated its functional role in NUP98-NSD1/FLT3-ITD immortalized primary murine hematopoietic cells by genetic and pharmacological targeting. Notably, SMARCA5 inhibition did not affect NUP98-NSD1 condensates suggesting that functional activity rather than condensate formation per se is crucial to maintain the transformed phenotype. Conclusions NUP98-NSD1 interacts and colocalizes on the genome with SMARCA5 which is an essential mediator of the NUP98-NSD1 transformation in hematopoietic cells. Formation of NUP98-NSD1 phase-separated nuclear condensates is not sufficient for the maintenance of transformed phenotype, which suggests that selective targeting of condensate constituents might represent a new therapeutic strategy for NUP98-NSD1 driven AML.

Abstract Axion-like particles (ALPs) are ubiquitous in models of new physics explaining some of the most pressing puzzles of the Standard Model. However, until relatively recently, little attention has been paid to its interplay with flavour. In this work, we study in detail the phenomenology of ALPs that exclusively interact with up-type quarks at the tree-level, which arise in some well-motivated ultra-violet completions such as QCD-like dark sectors or Froggatt-Nielsen type models of flavour. Our study is performed in the low-energy effective theory to highlight the key features of these scenarios in a model independent way. We derive all the existing constraints on these models and demonstrate how upcoming experiments at fixed-target facilities and the LHC can probe regions of the parameter space which are currently not excluded by cosmological and astrophysical bounds. We also emphasize how a future measurement of the currently unavailable meson decay D → π + invisible could complement these upcoming searches.

ABSTRACT Moloney murine leukemia virus (MLV) infects BALB/c mice and induces T-cell lymphoma in mice. Retroviral integration is mediated by the interaction of the MLV integrase (IN) with members of the bromodomain and extraterminal motif (BET) protein family (BRD2, BRD3, and BRD4). The introduction of the W390A mutation into MLV IN abolishes the BET interaction. Here, we compared the replication of W390A MLV to that of wild-type (WT) MLV in adult BALB/c mice to study the role of BET proteins in replication, integration, and tumorigenesis in vivo. Comparing WT and W390A MLV infections revealed similar viral loads in the blood, thymus, and spleen cells. Interestingly, W390A MLV integration was retargeted away from GC-enriched genomic regions. However, both WT MLV- and W390A MLV-infected mice developed T-cell lymphoma after similar latencies represented by an enlarged thymus and spleen and multiorgan tumor infiltration. Integration site sequencing from splenic tumor cells revealed clonal expansion in all WT MLV- and W390A MLV-infected mice. However, the integration profiles of W390A MLV and WT MLV differed significantly. Integrations were enriched in enhancers and promoters, but compared to the WT, W390A MLV integrated less frequently into enhancers and more frequently into oncogene bodies such as Notch1 and Ppp1r16b. We conclude that host factors direct MLV in vivo integration site selection. Although BET proteins target WT MLV integration preferentially toward enhancers and promoters, insertional lymphomagenesis can occur independently from BET, likely due to the intrinsically strong enhancer/promoter of the MLV long terminal repeat (LTR). IMPORTANCE In this study, we have shown that the in vivo replication of murine leukemia virus happens independently of BET proteins, which are key host determinants involved in retroviral integration site selection. This finding opens a new research line in the discovery of alternative viral or host factors that may complement the dominant host factor. In addition, our results show that BET-independent murine leukemia virus uncouples insertional mutagenesis from gene enhancers, although lymphomagenesis still occurs despite the lack of an interaction with BET proteins. Our findings also have implications for the engineering of BET-independent MLV-based vectors for gene therapy, which may not be a safe alternative.

In organic chemistry, synthetic routes for new molecules are often specified in terms of reacting molecules only. The current work reports an artificial intelligence model to predict the full sequence of experimental operations for an arbitrary chemical equation.

Abstract We explore the constraints current collider searches place on a QCD-like dark sector. A combination of multi-jet, multi-jet plus missing energy and emerging jets searches is used to derive constraints on the mediator mass across the full range of the dark meson lifetimes for the first time. The dark sector inherits a flavour structure from the coupling between the dark quarks and the SM quarks through the mediator. When this is taken into account, the differently flavoured dark pions become distinguishable through their lifetime. We show that also in these cases the above mentioned searches remain sensitive, and we obtain limits on the mediator mass also for the flavoured scenario. We then contrast the constraints from collider searches with direct detection bounds on the dark matter candidate itself in both the flavoured and unflavoured scenario. Using a simple prescription it becomes possible to display all constraints in the dark matter and mediator mass plane. Constraints from direct detection tend to be stronger than the collider constraints, unless the coupling to the first generation quarks is suppressed, in which case the collider searches place the most stringent limits on the parameter space.

The existence of sol–gel transitions is one of the most manifest properties of molecular gels. These transitions reflect their nature since they correspond to the association or dissociation of low weight molecules through non-covalent interactions to form the network constitutive of the gel. Most described molecular gels undergo only one gel-to-sol transition upon heating, and the reverse sol-to-gel transition upon cooling. It has been long observed that different conditions of formation could lead to gels with different morphologies, and that gels can undergo a transition from gel to crystals. However, more recent publications report molecular gels which exhibit additional transitions, for instance gel-to-gel transitions. This review surveys the molecular gels for which, in addition to sol–gel transitions, transitions of different nature have been reported: gel-to-gel transitions, gel-to-crystal transition, liquid–liquid phase separations, eutectic transformations, and synereses.