QCR7 affects the virulence of Candida albicans and the uptake of multiple carbon sources present in different host niches

BackgroundCandida albicans is a commensal yeast that may cause life-threatening infections. Studies have shown that the cytochrome b-c1 complex subunit 7 gene (QCR7) of C. albicans encodes a protein that forms a component of the mitochondrial electron transport chain complex III, making it an important target for studying the virulence of this yeast. However, to the best of our knowledge, the functions of QCR7 have not yet been characterized.MethodsA QCR7 knockout strain was constructed using SN152, and BALb/c mice were used as model animals to determine the role of QCR7 in the virulence of C. albicans. Subsequently, the effects of QCR7 on mitochondrial functions and use of carbon sources were investigated. Next, its mutant biofilm formation and hyphal growth maintenance were compared with those of the wild type. Furthermore, the transcriptome of the qcr7Δ/Δ mutant was compared with that of the WT strain to explore pathogenic mechanisms.ResultsDefective QCR7 reduced recruitment of inflammatory cells and attenuated the virulence of C. albicans infection in vivo. Furthermore, the mutant influenced the use of multiple alternative carbon sources that exist in several host niches (GlcNAc, lactic acid, and amino acid, etc.). Moreover, it led to mitochondrial dysfunction. Furthermore, the QCR7 knockout strain showed defects in biofilm formation or the maintenance of filamentous growth. The overexpression of cell-surface-associated genes (HWP1, YWP1, XOG1, and SAP6) can restore defective virulence phenotypes and the carbon-source utilization of qcr7Δ/Δ.ConclusionThis study provides new insights into the mitochondria-based metabolism of C. albicans, accounting for its virulence and the use of variable carbon sources that promote C. albicans to colonize host niches

Dynamics and Impacts of Human-Algorithm Consensus in Logistics Scheduling: Evidence from A Field Experiment

Algorithms are being implemented to aid human decision-making and most studies on human-algorithm interactions focus on how to improve human-algorithm cooperation. However, excessive reliance on algorithms in decision-making may hinder the complementary value of humans and algorithms. There is a lack of empirical evidence on the impacts of human-algorithm consensus in collaborative decision-making. To address this gap, this paper reports a large-scale field experiment conducted by one of China\u27s largest logistics firms in the context of route scheduling. The experiment involved assigning routes to either a treatment group, where algorithms and human operators collaborated in decision-making, or a control group, where human operators made decisions independently. We plan to collect data to evaluate the effects of algorithm implementation and to analyze the patterns and effects of human-algorithm consensus in a long-term cooperation. Our study aims to contribute to the literature on human-algorithm interactions in operational decisions

Phylogeny of the Ciliate Family Psilotrichidae (Protista, Ciliophora), a Curious and Poorly-Known Taxon, with Notes on Two Algae-Bearing Psilotrichids from Guam, USA

Background: The classification of the family Psilotrichidae, a curious group of ciliated protists with unique morphological and ontogenetic features, is ambiguous and poorly understood particularly due to the lack of molecular data. Hence, the systematic relationship between this group and other taxa in the subclass Hypotrichia remains unresolved. In this paper the morphology and phylogenetics of species from two genera of Psilotrichida are studied to shed new light on the phylogeny and species diversity of this group of ciliates. Results: The 18S rRNA gene sequences of species from two psilotrichid genera were obtained. In the phylogenetic trees, the available psilotrichid sequences are placed in a highly supported clade, justifying the establishment of the family Psilotrichidae. The morphology of two little-known species, packed with green algae, including a new species, Hemiholosticha kahli nov. spec., and Psilotrichides hawaiiensis Heber et al., 2018, is studied based on live observation, protargol impregnation, and scanning electron microscopy. Both species are easily recognized by their green coloration due to the intracellular algae, and a comprehensive discussion as to the possible roles of the intracellular algae is provided. Conclusions: The 18S rRNA gene phylogeny supports the morphological argument that Hemiholosticha, Psilotrichides and Urospinula belong to the same family, Psilotrichidae. However, the single-gene analysis, not surprisingly, does not resolve the deeper relationships of Psilotrichidae within the subclass Hypotrichia. Two littleknown psilotrichid genera with green algae were collected from the same puddle on the island of Guam, indicating a high species diversity and broader geographic distribution of this group of ciliates than previously supposed. Phylogenetic inferences from transcriptomic and/or genomic data will likely be necessary to better define the systematic position and evolution of the family Psilotrichidae. Further studies are also needed to clarify the role of the intracellular eyespot-bearing algae in these ciliates

NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition

Neural networks have shown great potential in accelerating the solution of partial differential equations (PDEs). Recently, there has been a growing interest in introducing physics constraints into training neural PDE solvers to reduce the use of costly data and improve the generalization ability. However, these physics constraints, based on certain finite dimensional approximations over the function space, must resolve the smallest scaled physics to ensure the accuracy and stability of the simulation, resulting in high computational costs from large input, output, and neural networks. This paper proposes a general acceleration methodology called NeuralStagger by spatially and temporally decomposing the original learning tasks into several coarser-resolution subtasks. We define a coarse-resolution neural solver for each subtask, which requires fewer computational resources, and jointly train them with the vanilla physics-constrained loss by simply arranging their outputs to reconstruct the original solution. Due to the perfect parallelism between them, the solution is achieved as fast as a coarse-resolution neural solver. In addition, the trained solvers bring the flexibility of simulating with multiple levels of resolution. We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations, which leads to an additional $10\sim100\times$ speed-up. Moreover, the experiment also shows that the learned model could be well used for optimal control.Comment: ICML 2023 accepte

Mechanical Properties of Recycled Concrete in Marine Environment

Experimental work was carried out to develop information about mechanical properties of recycled concrete (RC) in marine environment. By using the seawater and dry-wet circulation to simulate the marine environment, specimens of RC were tested with different replacement percentages of 0%, 30%, and 60% after immersing in seawater for 4, 8, 12, and 16 months, respectively. Based on the analysis of the stress-strain curves (SSCs) and compressive strength, it is revealed that RC’ peak value and elastic modulus decreased with the increase of replacement percentage and corroding time in marine environment. And the failure of recycled concrete was speeded up with more obvious cracks and larger angles of 65° to 85° in the surface when compared with normal concrete. Finally, the grey model (GM) with equal time intervals was constructed to investigate the law of compressive strength of recycled concrete in marine environment, and it is found that the GM is accurate and feasible for the prediction of RC compressive strength in marine environment