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This work presents a novel technique for constructing spatially resolved ion densities from Transient Insertion Langmuir Probe (TIL Probe) measurements in a flame. Similar to a tomographic transformation, this technique is used to deduce the spatial distribution of ions in a flame from many individual measurements that are integrated along a probe's length. We demonstrate the approach in the oxyfuel cutting torch preheat flame, which presents two severe challenges for electrical measurements: (1) temperatures over 3,000K destroy most probes made from alloys with appropriate chemical stability, and (2) the relevant length scales are on the order 0.15 mm. Presented here are (1) a Fourier series formulation for the current density, (2) a least-square problem for calculating the coefficients, (3) criteria for the highest wavenumber allowed in the expansion, (4) description of an experiment used to measure probe currents in an oxyfuel cutting torch preheat flame, (5) solution for spatially resolved current density in the oxyfuel cutting torch flame. Images of ion current density are produced with a resolution of 0.15 mm (0.0059 in), exhibiting peak current densities around 14 $$\mu$$A/mm. It is found that low-signal regions in the ``shadow'' of high-signal regions can suffer from signal-to-noise ratio problems due to natural fluctuations in the flame, and improvements are proposed to mitigate the effect. It is found that the numerical cost of setting up the resulting Hermitian-matrix linear problem far exceeds the numerical cost of inversion. High-level packages like Python and MATLAB are far too slow, so a multi-threaded algorithm is implemented in C, and the LAPACKE C library is used for efficient linear algebra support. 

This work presents a novel technique for constructing spatially resolved ion densities from Transient Insertion Langmuir Probe (TIL Probe) measurements in a flame. Similar to a tomographic transformation, this technique is used to deduce the spatial distribution of ions in a flame from many individual measurements that are integrated along a probe's length. We demonstrate the approach in the oxyfuel cutting torch preheat flame, which presents two severe challenges for electrical measurements: (1) temperatures over 3,000K destroy most probes made from alloys with appropriate chemical stability, and (2) the relevant length scales are on the order 0.15 mm. Presented here are (1) a Fourier series formulation for the current density, (2) a least-square problem for calculating the coefficients, (3) criteria for the highest wavenumber allowed in the expansion, (4) description of an experiment used to measure probe currents in an oxyfuel cutting torch preheat flame, (5) solution for spatially resolved current density in the oxyfuel cutting torch flame. Images of ion current density are produced with a resolution of 0.15 mm (0.0059 in), exhibiting peak current densities around 14 $$\mu$$A/mm. It is found that low-signal regions in the ``shadow'' of high-signal regions can suffer from signal-to-noise ratio problems due to natural fluctuations in the flame, and improvements are proposed to mitigate the effect. It is found that the numerical cost of setting up the resulting Hermitian-matrix linear problem far exceeds the numerical cost of inversion. High-level packages like Python and MATLAB are far too slow, so a multi-threaded algorithm is implemented in C, and the LAPACKE C library is used for efficient linear algebra support. 

Galectins are a large and diverse protein family defined by the presence of a carbohydrate recognition domain (CRD) that binds β-galactosides. They play important roles in early development, tissue regeneration, immune homeostasis, pathogen recognition, and cancer. In many cases, studies that examine galectin biology and the effect of manipulating galectins are aided by, or require the ability to express and purify, specific members of the galectin family. In many cases, E. coli is employed as a heterologous expression system, and galectin expression is induced with isopropyl β-galactoside (IPTG). Here, we show that galectin-3 recognizes IPTG with micromolar affinity and that as IPTG induces expression, newly synthesized galectin can bind and sequester cytosolic IPTG, potentially repressing further expression. To circumvent this putative inhibitory feedback loop, we utilized an autoinduction protocol that lacks IPTG, leading to significantly increased yields of galectin-3. Much of this work was done within the context of a course-based undergraduate research experience, indicating the ease and reproducibility of the resulting expression and purification protocols. 

Abstract Despite the f₀(980) hadron having been discovered half a century ago, the question about its quark content has not been settled: it might be an ordinary quark-antiquark ($${{\rm{q}}}\overline{{{\rm{q}}}}$$ $q\overline{q}$) meson, a tetraquark ($${{\rm{q}}}\overline{{{\rm{q}}}}{{\rm{q}}}\overline{{{\rm{q}}}}$$ $q\overline{q}q\overline{q}$) exotic state, a kaon-antikaon ($${{\rm{K}}}\overline{{{\rm{K}}}}$$ $K\overline{K}$) molecule, or a quark-antiquark-gluon ($${{\rm{q}}}\overline{{{\rm{q}}}}{{\rm{g}}}$$ $q\overline{q}g$) hybrid. This paper reports strong evidence that the f₀(980) state is an ordinary$${{\rm{q}}}\overline{{{\rm{q}}}}$$ $q\overline{q}$meson, inferred from the scaling of elliptic anisotropies (v₂) with the number of constituent quarks (n_q), as empirically established using conventional hadrons in relativistic heavy ion collisions. The f₀(980) state is reconstructed via its dominant decay channel f₀(980) →π⁺π⁻, in proton-lead collisions recorded by the CMS experiment at the LHC, and itsv₂is measured as a function of transverse momentum (p_T). It is found that then_q= 2 ($${{\rm{q}}}\overline{{{\rm{q}}}}$$ $q\overline{q}$state) hypothesis is favored overn_q= 4 ($${{\rm{q}}}\overline{{{\rm{q}}}}{{\rm{q}}}\overline{{{\rm{q}}}}$$ $q\overline{q}q\overline{q}$or$${{\rm{K}}}\overline{{{\rm{K}}}}$$ $K\overline{K}$states) by 7.7, 6.3, or 3.1 standard deviations in thep_T< 10, 8, or 6 GeV/cranges, respectively, and overn_q= 3 ($${{\rm{q}}}\overline{{{\rm{q}}}}{{\rm{g}}}$$ $q\overline{q}g$hybrid state) by 3.5 standard deviations in thep_T< 8 GeV/crange. This result represents the first determination of the quark content of the f₀(980) state, made possible by using a novel approach, and paves the way for similar studies of other exotic hadron candidates. 

ABSTRACT We describe the discovery of an archaeal virus, one that infects archaea, tentatively named Thermoproteus spherical piliferous virus 1 (TSPV1), which was purified from a Thermoproteales host isolated from a hot spring in Yellowstone National Park (USA). TSPV1 packages an 18.65-kb linear double-stranded DNA (dsDNA) genome with 31 open reading frames (ORFs), whose predicted gene products show little homology to proteins with known functions. A comparison of virus particle morphologies and gene content demonstrates that TSPV1 is a new member of the Globuloviridae family of archaeal viruses. However, unlike other Globuloviridae members, TSPV1 has numerous highly unusual filaments decorating its surface, which can extend hundreds of micrometers from the virion. To our knowledge, similar filaments have not been observed in any other archaeal virus. The filaments are remarkably stable, remaining intact across a broad range of temperature and pH values, and they are resistant to chemical denaturation and proteolysis. A major component of the filaments is a glycosylated 35-kDa TSPV1 protein (TSPV1 GP24). The filament protein lacks detectable homology to structurally or functionally characterized proteins. We propose, given the low host cell densities of hot spring environments, that the TSPV1 filaments serve to increase the probability of virus attachment and entry into host cells. IMPORTANCE High-temperature environments have proven to be an important source for the discovery of new archaeal viruses with unusual particle morphologies and gene content. Our isolation of Thermoproteus spherical piliferous virus 1 (TSPV1), with numerous filaments extending from the virion surface, expands our understanding of viral diversity and provides new insight into viral replication in high-temperature environments. 

Abstract A search is presented for the pair production of new heavy resonances, each decaying into a top quark (t) or antiquark and a gluon (g). The analysis uses data recorded with the CMS detector from proton–proton collisions at a center-of-mass energy of 13$$\,\text {Te}\hspace{-.08em}\text {V}$$ $\text{Te}\text{V}$at the LHC, corresponding to an integrated luminosity of 138$$\,\text {fb}^{-1}$$ ${\text{fb}}^{-1}$. Events with one muon or electron, multiple jets, and missing transverse momentum are selected. After using a deep neural network to enrich the data sample with signal-like events, distributions in the scalar sum of the transverse momenta of all reconstructed objects are analyzed in the search for a signal. No significant deviations from the standard model prediction are found. Upper limits at 95% confidence level are set on the product of cross section and branching fraction squared for the pair production of excited top quarks in the$$\text {t}^{*} \rightarrow {\text {t}} {\text {g}} $$ $\text{t}_{}^{\ast }\to \text{tg}$decay channel. The upper limits range from 120 to 0.8$$\,\text {fb}$$ $\text{fb}$for a$$\text {t}^{*} $$ $\text{t}_{}^{\ast }$with spin-1/2 and from 15 to 1.0$$\,\text {fb}$$ $\text{fb}$for a$$\text {t}^{*} $$ $\text{t}_{}^{\ast }$with spin-3/2. These correspond to mass exclusion limits up to 1050 and 1700$$\,\text {Ge}\hspace{-.08em}\text {V}$$ $\text{Ge}\text{V}$for spin-1/2 and spin-3/2$$\text {t}^{*} $$ $\text{t}_{}^{\ast }$particles, respectively. These are the most stringent limits to date on the existence of$$\text {t}^{*} \rightarrow {\text {t}} {\text {g}} $$ $\text{t}_{}^{\ast }\to \text{tg}$resonances. 

Abstract A measurement of the dijet production cross section is reported based on proton–proton collision data collected in 2016 at$$\sqrt{s}=13\,\text {Te}\hspace{-.08em}\text {V} $$ $\sqrt{s}=13\mathrm{Te}V$by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of up to 36.3$$\,\text {fb}^{-1}$$ ${\mathrm{fb}}^{-1}$. Jets are reconstructed with the anti-$$k_{\textrm{T}} $$ $k_{\text{T}}$algorithm for distance parameters of$$R=0.4$$ $R=0.4$and 0.8. Cross sections are measured double-differentially (2D) as a function of the largest absolute rapidity$$|y |_{\text {max}} $$ ${\left|y\right|}_{\max }$of the two jets with the highest transverse momenta$$p_{\textrm{T}}$$ $p_{\text{T}}$and their invariant mass$$m_{1,2} $$ $m_{1,2}$, and triple-differentially (3D) as a function of the rapidity separation$$y^{*} $$ $y^{\ast }$, the total boost$$y_{\text {b}} $$ $y_b$, and either$$m_{1,2} $$ $m_{1,2}$or the average$$p_{\textrm{T}}$$ $p_{\text{T}}$of the two jets. The cross sections are unfolded to correct for detector effects and are compared with fixed-order calculations derived at next-to-next-to-leading order in perturbative quantum chromodynamics. The impact of the measurements on the parton distribution functions and the strong coupling constant at the mass of the$${\text {Z}} $$ $Z$boson is investigated, yielding a value of$$\alpha _\textrm{S} (m_{{\text {Z}}}) =0.1179\pm 0.0019$$ ${\alpha }_{\text{S}}\left(m_Z\right)=0.1179\pm 0.0019$. 

Abstract 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.