AIMS: The influence of bacterial species/strains in agitated culture was investigated on the morphology and structure characteristics of bacterial cellulose. METHODS AND RESULTS: Komagataeibacter ...nataicola Y19 and Gluconacetobacter entanii ACCC10215 were inoculated in Hestrin–Schramm (HS) medium and subjected to agitated cultivation. Different kinds of BCs were obtained including flocky asterisk‐like BC by G. entanii ACCC10215 and solid sphere‐like BC by K. nataicola Y19. The SEM results showed that the asterisk‐like BC had larger pores than the solid sphere‐like BC. The FT‐IR and X‐ray diffraction results showed the asterisk‐like BC had lower crystallinity (81·43%), higher cellulose Iα mass fraction (79·74%) and smaller crystallite size. CONCLUSIONS: The different species/strains can influence the morphology and structure characteristics of BC in agitated culture. SIGNIFICANCE AND IMPACT OF THE STUDY: We examined the influence of different species/strains on the morphology, macro‐ and microstructure of BCs produced in agitated culture for the first time, which suggest that different BCs with potential applications could be obtained by choosing different species or strains and fermentation method.
We report a study of the processes of e^{+}e^{-}→K^{+}D_{s}^{-}D^{*0} and K^{+}D_{s}^{*-}D^{0} based on e^{+}e^{-} annihilation samples collected with the BESIII detector operating at BEPCII at five ...center-of-mass energies ranging from 4.628 to 4.698 GeV with a total integrated luminosity of 3.7 fb^{-1}. An excess of events over the known contributions of the conventional charmed mesons is observed near the D_{s}^{-}D^{*0} and D_{s}^{*-}D^{0} mass thresholds in the K^{+} recoil-mass spectrum for events collected at sqrts=4.681 GeV. The structure matches a mass-dependent-width Breit-Wigner line shape, whose pole mass and width are determined as (3982.5_{-2.6}^{+1.8}±2.1) MeV/c^{2} and (12.8_{-4.4}^{+5.3}±3.0) MeV, respectively. The first uncertainties are statistical and the second are systematic. The significance of the resonance hypothesis is estimated to be 5.3 σ over the contributions only from the conventional charmed mesons. This is the first candidate for a charged hidden-charm tetraquark with strangeness, decaying into D_{s}^{-}D^{*0} and D_{s}^{*-}D^{0}. However, the properties of the excess need further exploration with more statistics.
High-β_{θe} (a ratio of the electron thermal pressure to the poloidal magnetic pressure) steady-state long-pulse plasmas with steep central electron temperature gradient are achieved in the ...Experimental Advanced Superconducting Tokamak. An intrinsic current is observed to be modulated by turbulence driven by the electron temperature gradient. This turbulent current is generated in the countercurrent direction and can reach a maximum ratio of 25% of the bootstrap current. Gyrokinetic simulations and experimental observations indicate that the turbulence is the electron temperature gradient mode (ETG). The dominant mechanism for the turbulent current generation is due to the divergence of ETG-driven residual flux of current. Good agreement has been found between experiments and theory for the critical value of the electron temperature gradient triggering ETG and for the level of the turbulent current. The maximum values of turbulent current and electron temperature gradient lead to the destabilization of an m/n=1/1 kink mode, which by counteraction reduces the turbulence level (m and n are the poloidal and toroidal mode number, respectively). These observations suggest that the self-regulation system including turbulence, turbulent current, and kink mode is a contributing mechanism for sustaining the steady-state long-pulse high-β_{θe} regime.
Large-scale, highly integrated and low-power-consuming hardware is becoming progressively more important for realizing optical neural networks (ONNs) capable of advanced optical computing. ...Traditional experimental implementations need N
units such as Mach-Zehnder interferometers (MZIs) for an input dimension N to realize typical computing operations (convolutions and matrix multiplication), resulting in limited scalability and consuming excessive power. Here, we propose the integrated diffractive optical network for implementing parallel Fourier transforms, convolution operations and application-specific optical computing using two ultracompact diffractive cells (Fourier transform operation) and only N MZIs. The footprint and energy consumption scales linearly with the input data dimension, instead of the quadratic scaling in the traditional ONN framework. A ~10-fold reduction in both footprint and energy consumption, as well as equal high accuracy with previous MZI-based ONNs was experimentally achieved for computations performed on the MNIST and Fashion-MNIST datasets. The integrated diffractive optical network (IDNN) chip demonstrates a promising avenue towards scalable and low-power-consumption optical computational chips for optical-artificial-intelligence.
The process e+e- → Λ Λ ¯ is studied using data samples at √s = 2.2324, 2.400, 2.800 and 3.080 GeV collected with the BESIII detector operating at the BEPCII collider. The Born cross section is ...measured at √s=2.2324 GeV, which is 1.0 MeV above the Λ Λ ¯ mass threshold, to be 305±$45_{-36}^{+66}$ pb, where the first uncertainty is statistical and the second systematic. The substantial cross section near threshold is significantly larger than that expected from theory, which predicts the cross section to vanish at threshold. The Born cross sections at √s=2.400, 2.800 and 3.080 GeV are measured and found to be consistent with previous experimental results, but with improved precision. Finally, the corresponding effective electromagnetic form factors of Λ are deduced.
The cross section for the process e^{+}e^{-}→π^{+}π^{-}J/ψ is measured precisely at center-of-mass energies from 3.77 to 4.60 GeV using 9 fb^{-1} of data collected with the BESIII detector operating ...at the BEPCII storage ring. Two resonant structures are observed in a fit to the cross section. The first resonance has a mass of (4222.0±3.1±1.4) MeV/c^{2} and a width of (44.1±4.3±2.0) MeV, while the second one has a mass of (4320.0±10.4±7.0) MeV/c^{2} and a width of (101.4_{-19.7}^{+25.3}±10.2) MeV, where the first errors are statistical and second ones are systematic. The first resonance agrees with the Y(4260) resonance reported by previous experiments. The precision of its resonant parameters is improved significantly. The second resonance is observed in e^{+}e^{-}→π^{+}π^{-}J/ψ for the first time. The statistical significance of this resonance is estimated to be larger than 7.6σ. The mass and width of the second resonance agree with the Y(4360) resonance reported by the BABAR and Belle experiments within errors. Finally, the Y(4008) resonance previously observed by the Belle experiment is not confirmed in the description of the BESIII data.
Gastroesophageal adenocarcinomas (GEAs) are heterogeneous cancers where immune checkpoint inhibitors have robust efficacy in heavily inflamed microsatellite instability (MSI) or Epstein-Barr virus ...(EBV)-positive subtypes. Immune checkpoint inhibitor responses are markedly lower in diffuse/genome stable (GS) and chromosomal instable (CIN) GEAs. In contrast to EBV and MSI subtypes, the tumor microenvironment of CIN and GS GEAs have not been fully characterized to date, which limits our ability to improve immunotherapeutic strategies.
Here we aimed to identify tumor-immune cell association across GEA subclasses using data from The Cancer Genome Atlas (N = 453 GEAs) and archival GEA resection specimen (N = 71). The Cancer Genome Atlas RNAseq data were used for computational inferences of immune cell subsets, which were correlated to tumor characteristics within and between subtypes. Archival tissues were used for more spatial immune characterization spanning immunohistochemistry and mRNA expression analyses.
Our results confirmed substantial heterogeneity in the tumor microenvironment between distinct subtypes. While MSI-high and EBV+ GEAs harbored most intense T cell infiltrates, the GS group showed enrichment of CD4+ T cells, macrophages and B cells and, in ∼50% of cases, evidence for tertiary lymphoid structures. In contrast, CIN cancers possessed CD8+ T cells predominantly at the invasive margin while tumor-associated macrophages showed tumor infiltrating capacity. Relatively T cell-rich ‘hot’ CIN GEAs were often from Western patients, while immunological ‘cold’ CIN GEAs showed enrichment of MYC and cell cycle pathways, including amplification of CCNE1.
These results reveal the diversity of immune phenotypes of GEA. Half of GS gastric cancers have tertiary lymphoid structures and are therefore promising candidates for immunotherapy. The majority of CIN GEAs, however, exhibit T cell exclusion and infiltrating macrophages. Associations of immune-poor CIN GEAs with MYC activity and CCNE1 amplification may enable new studies to determine precise mechanisms of immune evasion, ultimately inspiring new therapeutic modalities.
•There is large heterogeneity in the immune contexture of gastroesophageal adenocarcinoma (GEA) subtypes.•Chromosomal instable GEAs are often T cell excluded, which is associated with enhanced MYC and cell cycle pathways.•Genome stable cancers, contrarily, often have tertiary lymphoid structures.•This study argues for more personalized immunotargeting strategies in gastroesophageal cancer treatment.
Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued ...representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.
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