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Bacterial chemotaxis is the directed movement of motile bacteria in gradients of chemoeffectors. This behavior is mediated by dedicated signal transduction pathways that couple environment sensing with changes in the direction of rotation of flagellar motors to ultimately affect the motility pattern. Azospirillum brasilense uses two distinct chemotaxis pathways, named Che1 and Che4, and four different response regulators (CheY1, CheY4, CheY6, and CheY7) to control the swimming pattern during chemotaxis. Each of the CheY homologs was shown to differentially affect the rotational bias of the polar flagellum and chemotaxis. The role, if any, of these CheY homologs in swarming, which depends on a distinct lateral flagella system or in attachment is not known. Here, we characterize CheY homologs’ roles in swimming, swarming, and attachment to abiotic and biotic (wheat roots) surfaces and biofilm formation. We show that while strains lacking CheY1 and CheY6 are still able to navigate air gradients, strains lacking CheY4 and CheY7 are chemotaxis null. Expansion of swarming colonies in the presence of gradients requires chemotaxis. The induction of swarming depends on CheY4 and CheY7, but the cells’ organization as dense clusters in productive swarms appear to depend on functional CheYs but not chemotaxis per se . Similarly, functional CheY homologs but not chemotaxis, contribute to attachment to both abiotic and root surfaces as well as to biofilm formation, although these effects are likely dependent on additional cell surface properties such as adhesiveness. Collectively, our data highlight distinct roles for multiple CheY homologs and for chemotaxis on swarming and attachment to surfaces. 

ABSTRACT Bacterial chemotaxis affords motile bacteria the ability to navigate the environment to locate niches for growth and survival. At the molecular level, chemotaxis depends on chemoreceptor signaling arrays that interact with cytoplasmic proteins to control the direction of movement. In Azospirillum brasilense , chemotaxis is mediated by two distinct chemotaxis pathways: Che1 and Che4. Both Che1 and Che4 are critical in the A. brasilense free-living and plant-associated lifestyles. Here, we use whole-cell proteomics and metabolomics to characterize the role of chemotaxis in A. brasilense physiology. We found that mutants lacking CheA1 or CheA4 or both are affected in nonchemotaxis functions, including major changes in transcription, signaling transport, and cell metabolism. We identify specific effects of CheA1 and CheA4 on nitrogen metabolism, including nitrate assimilation and nitrogen fixation, that may depend, at least, on the transcriptional control of rpoN , which encodes RpoN, a global regulator of metabolism, including nitrogen. Consistent with proteomics, the abundance of several nitrogenous compounds (purines, pyrimidines, and amino acids) changed in the metabolomes of the chemotaxis mutants relative to the parental strain. Further, we uncover novel, and yet uncharacterized, layers of transcriptional and posttranscriptional control of nitrogen metabolism regulators. Together, our data reveal roles for CheA1 and CheA4 in linking chemotaxis and nitrogen metabolism, likely through control of global regulatory networks. IMPORTANCE Bacterial chemotaxis is widespread in bacteria, increasing competitiveness in diverse environments and mediating associations with eukaryotic hosts ranging from commensal to beneficial and pathogenic. In most bacteria, chemotaxis signaling is tightly linked to energy metabolism, with this coupling occurring through the sensory input of several energy-sensing chemoreceptors. Here, we show that in A. brasilense the chemotaxis proteins have key roles in modulating nitrogen metabolism, including nitrate assimilation and nitrogen fixation, through novel and yet unknown regulations. These results are significant given that A. brasilense is a model bacterium for plant growth promotion and free-living nitrogen fixation and is used as a bio-inoculant for cereal crops. Chemotaxis signaling in A. brasilense thus links locomotor behaviors to nitrogen metabolism, allowing cells to continuously and reciprocally adjust metabolism and chemotaxis signaling as they navigate gradients. 

We report a search for a heavy neutral lepton (HNL) that mixes predominantly with${\nu }_{\tau }$. The search utilizes data collected with the Belle detector at the KEKB asymmetric energy$e^{+}{e}^{-}$collider. The data sample was collected at and just below the center-of-mass energies of the$\mathrm{\Upsilon }(4S)$and$\mathrm{\Upsilon }(5S)$resonances and has an integrated luminosity of$915{\mathrm{fb}}^{-1}$, corresponding to$(836\pm 12)\times {10}^6$$e^{+}{e}^{-}\to {\tau }^{+}{\tau }^{-}$events. We search for production of the HNL (denoted$N$) in the decay${\tau }^{-}\to {\pi }^{-}N$followed by its decay via$N\to {\mu }^{+}{\mu }^{-}{\nu }_{\tau }$. The search focuses on the parameter-space region in which the HNL is long-lived, so that the${\mu }^{+}{\mu }^{-}$originate from a common vertex that is significantly displaced from the collision point of the KEKB beams. Consistent with the expected background yield, one event is observed in the data sample after application of all the event-selection criteria. We report limits on the mixing parameter of the HNL with the$\tau$neutrino as a function of the HNL mass.

Published by the American Physical Society2024 

We measure the branching fraction of the decay$B^{-}\to D^0\rho (770{)}^{-}$using data collected with the Belle II detector. The data contain 387 million$B\overline{B}$pairs produced in$e^{+}{e}^{-}$collisions at the$\mathrm{\Upsilon }(4S)$resonance. We reconstruct$8360\pm 180$decays from an analysis of the distributions of the$B^{-}$energy and the$\rho (770{)}^{-}$helicity angle. We determine the branching fraction to be$(0.939\pm 0.021(\mathrm{stat})\pm 0.050(\mathrm{syst}))\%$, in agreement with previous results. Our measurement improves the relative precision of the world average by more than a factor of two.

Published by the American Physical Society2024 

We report on a search for a resonance$X$decaying to a pair of muons in$e^{+}{e}^{-}\to {\mu }^{+}{\mu }^{-}X$events in the$0.212–9.000\mathrm{GeV}/c^2$mass range, using$178{\mathrm{fb}}^{-1}$of data collected by the Belle II experiment at the SuperKEKB collider at a center of mass energy of 10.58 GeV. The analysis probes two different models of$X$beyond the standard model: a$Z^{\prime }$vector boson in the$L_{\mu }-L_{\tau }$model and a muonphilic scalar. We observe no evidence for a signal and set exclusion limits at the 90% confidence level on the products of cross section and branching fraction for these processes, ranging from 0.046 fb to 0.97 fb for the$L_{\mu }-L_{\tau }$model and from 0.055 fb to 1.3 fb for the muonphilic scalar model. For masses below$6\mathrm{GeV}/c^2$, the corresponding constraints on the couplings of these processes to the standard model range from 0.0008 to 0.039 for the$L_{\mu }-L_{\tau }$model and from 0.0018 to 0.040 for the muonphilic scalar model. These are the first constraints on the muonphilic scalar from a dedicated search.

Published by the American Physical Society2024 

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.