Atomic and magnetic structures of (CuCl)LaNb2_2O7_7 and (CuBr)LaNb2_2O7_7: Density functional calculations

The atomic and magnetic structures of (Cu$X$)LaNb$_2$O$_7$ ($X$=Cl and Br) are investigated using the density-functional calculations. Among several dozens of examined structures, an orthorhombic distorted $2\times 2$ structure, in which the displacement pattern of $X$ halogens resembles the model conjectured previously based on the empirical information is identified as the most stable one. The displacements of $X$ halogens, together with those of Cu ions, result in the formation of $X$-Cu-$X$-Cu-$X$ zigzag chains in the two materials. The nearest-neighbor interaction within the zigzag chains are determined to be antiferromagnetic (AFM) for (CuCl)LaNb$_2$O$_7$ but ferromagnetic (FM) for (CuBr)LaNb$_2$O$_7$. On the other hand, the first two neighboring interactions between the Cu cations from adjacent chains are found to be AFM and FM respectively for both compounds. The magnitudes of all these in-plane exchange couplings in (CuBr)LaNb$_2$O$_7$ are evaluated to be about three times those in (CuCl)LaNb$_2$O$_7$. In addition, a sizable AFM inter-plane interaction is found between the Cu ions separated by two NbO$_6$ octahedra. The present study strongly suggests the necessity to go beyond the square $J_1-J_2$ model in order to correctly account for the magnetic property of (Cu$X)$LaNb$_2$O$_7$.Comment: 24 pages, 7 figure

S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds

With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a trend in academia and industry alike. Prior attempts on the challenging Semantic Scene Completion task - which entails the inference of dense 3D structure and associated semantic labels from "sparse" representations - have been, to a degree, successful in small indoor scenes when provided with dense point clouds or dense depth maps often fused with semantic segmentation maps from RGB images. However, the performance of these systems drop drastically when applied to large outdoor scenes characterized by dynamic and exponentially sparser conditions. Likewise, processing of the entire sparse volume becomes infeasible due to memory limitations and workarounds introduce computational inefficiency as practitioners are forced to divide the overall volume into multiple equal segments and infer on each individually, rendering real-time performance impossible. In this work, we formulate a method that subsumes the sparsity of large-scale environments and present S3CNet, a sparse convolution based neural network that predicts the semantically completed scene from a single, unified LiDAR point cloud. We show that our proposed method outperforms all counterparts on the 3D task, achieving state-of-the art results on the SemanticKITTI benchmark. Furthermore, we propose a 2D variant of S3CNet with a multi-view fusion strategy to complement our 3D network, providing robustness to occlusions and extreme sparsity in distant regions. We conduct experiments for the 2D semantic scene completion task and compare the results of our sparse 2D network against several leading LiDAR segmentation models adapted for bird's eye view segmentation on two open-source datasets.Comment: 14 page

Genetic liability of gut microbiota for idiopathic pulmonary fibrosis and lung function: a two-sample Mendelian randomization study

BackgroundThe microbiota-gut-lung axis has elucidated a potential association between gut microbiota and idiopathic pulmonary fibrosis (IPF). However, there is a paucity of population-level studies with providing robust evidence for establishing causality. This two-sample Mendelian randomization (MR) analysis aimed to investigate the causal relationship between the gut microbiota and IPF as well as lung function.Materials and methodsAdhering to Mendel’s principle of inheritance, this MR analysis utilized summary-level data from respective genome-wide association studies (GWAS) involving 211 gut microbial taxa, IPF, and lung function indicators such as FEV1, FVC, and FEV1/FVC. A bidirectional two-sample MR design was employed, utilizing multiple MR analysis methods, including inverse variance-weighted (IVW), weighted median, MR-Egger, and weighted mode. Multivariable MR (MVMR) was used to uncover mediating factors connecting the exposure and outcome. Additionally, comprehensive sensitivity analyses were conducted to ensure the robustness of the results.ResultsThe MR results confirmed four taxa were found causally associated with the risk of IPF. Order Bifidobacteriales (OR=0.773, 95% CI: 0.610–0.979, p=0.033), Family Bifidobacteriaceae (OR=0.773, 95% CI: 0.610–0.979, p=0.033), and Genus RuminococcaceaeUCG009 (OR=0.793, 95% CI: 0.652–0.965, p=0.020) exerted protective effects on IPF, while Genus Coprococcus2 (OR=1.349, 95% CI: 1.021–1.783, p=0.035) promote the development of IPF. Several taxa were causally associated with lung function, with those in Class Deltaproteobacteria, Order Desulfovibrionales, Family Desulfovibrionaceae, Class Verrucomicrobiae, Order Verrucomicrobiales and Family Verrucomicrobiaceae being the most prominent beneficial microbiota, while those in Family Lachnospiraceae, Genus Oscillospira, and Genus Parasutterella were associated with impaired lung function. As for the reverse analysis, MR results confirmed the effects of FEV1 and FVC on the increased abundance of six taxa (Phylum Actinobacteria, Class Actinobacteria, Order Bifidobacteriales, Family Bifidobacteriaceae, Genus Bifidobacterium, and Genus Ruminiclostridium9) with a boosted level of evidence. MVMR suggested monounsaturated fatty acids, total fatty acids, saturated fatty acids, and ratio of omega-6 fatty acids to total fatty acids as potential mediating factors in the genetic association between gut microbiota and IPF.ConclusionThe current study suggested the casual effects of the specific gut microbes on the risk of IPF and lung function. In turn, lung function also exerted a positive role in some gut microbes. A reasonable dietary intake of lipid substances has a certain protective effect against the occurrence and progression of IPF. This study provides novel insights into the potential role of gut microbiota in IPF and indicates a possible gut microbiota-mediated mechanism for the prevention of IPF

First report on the occurrence of Rickettsia slovaca and Rickettsia raoultii in Dermacentor silvarum in China

10.1186/1756-3305-5-19Parasites and Vectors511

Mychonastes afer HSO-3-1 as a potential new source of biodiesel

<p>Abstract</p> <p>Background</p> <p>Biodiesel is considered to be a promising future substitute for fossil fuels, and microalgae are one source of biodiesel. The ratios of lipid, carbohydrates and proteins are different in different microalgal species, and finding a good strain for oil production remains a difficult prospect. Strains producing valuable co-products would improve the viability of biofuel production.</p> <p>Results</p> <p>In this study, we performed sequence analysis of the 18S rRNA gene and internal transcribed spacer (ITS) of an algal strain designated HSO-3-1, and found that it was closely related to the <it>Mychonastes afer </it>strain CCAP 260/6. Morphology and cellular structure observation also supported the identification of strain HSO-3-1 as <it>M. afer</it>. We also investigated the effects of nitrogen on the growth and lipid accumulation of the naturally occurring <it>M. afer </it>HSO-3-1, and its potential for biodiesel production. In total, 17 fatty acid methyl esters (FAMEs) were identified in <it>M. afer </it>HSO-3-1, using gas chromatography/mass spectrometry. The total lipid content of <it>M. afer </it>HSO-3-1 was 53.9% of the dry cell weight, and we also detected nervonic acid (C24:1), which has biomedical applications, making up 3.8% of total fatty acids. The highest biomass and lipid yields achieved were 3.29 g/l and 1.62 g/l, respectively, under optimized conditions.</p> <p>Conclusion</p> <p>The presence of octadecenoic and hexadecanoic acids as major components, with the presence of a high-value component, nervonic acid, renders <it>M. afer </it>HSO-3-1 biomass an economic feedstock for biodiesel production.</p

Higgs Boson Production and Decay in Little Higgs Models with T-parity

We study Higgs boson production and decay in a certain class of Little Higgs models with T-parity in which some T-parity partners of the Standard Model (SM) fermions gain their masses through Yukawa-type couplings. We find that the Higgs boson production cross section of a 120 GeV Higgs boson at the CERN LHC via gg fusion process at one-loop level could be reduced by about 45%, 35% and 20%, as compared to its SM prediction, for a relatively low new particle mass scale f = 600, 700 and 1000 GeV, respectively. On the other hand, the weak boson fusion cross section is close to the SM value. Furthermore, the Higgs boson decay branching ratio into di-photon mode can be enhanced by about 35% in small Higgs mass region in certain case, for the total decay width of Higgs boson in the Little Higgs model is always smaller than that in the SM.Comment: Replaced with version to appear in Phys. Lett. B, typos corrected and references adde

SNP-S3: Shared Network Pre-training and Significant Semantic Strengthening for Various Video-Text Tasks

We present a framework for learning cross-modal video representations by directly pre-training on raw data to facilitate various downstream video-text tasks. Our main contributions lie in the pre-training framework and proxy tasks. First, based on the shortcomings of two mainstream pixel-level pre-training architectures (limited applications or less efficient), we propose Shared Network Pre-training (SNP). By employing one shared BERT-type network to refine textual and cross-modal features simultaneously, SNP is lightweight and could support various downstream applications. Second, based on the intuition that people always pay attention to several "significant words" when understanding a sentence, we propose the Significant Semantic Strengthening (S3) strategy, which includes a novel masking and matching proxy task to promote the pre-training performance. Experiments conducted on three downstream video-text tasks and six datasets demonstrate that, we establish a new state-of-the-art in pixel-level video-text pre-training; we also achieve a satisfactory balance between the pre-training efficiency and the fine-tuning performance. The codebase are available at https://github.com/alipay/Ant-Multi-Modal-Framework/tree/main/prj/snps3_vtp.Comment: Accepted by TCSVT (IEEE Transactions on Circuits and Systems for Video Technology

Thermal properties of coal during low temperature oxidation using a grey correlation method

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The low temperature oxidation of coal is a contradictory and unified dynamic process of coexisting mass and heat transfer. The thermophysical properties are crucial during coal spontaneous combustion. In the current paper, the variations of moisture, ash, volatiles, fixed carbon and thermophysical properties (thermal diffusivity, specific heat and thermal conductivity) of three coal samples from 30 °C to 300 °C were studied, and their grey correlation was analyzed. The results indicated that with the increase of temperature, the free moisture of Coals A and B decreased first but then increased, while the free moisture of Coal C kept decreasing without a later increase. The variation of surface moisture was consistent with that of free moisture. The trend of volatiles and fixed carbon was completely the opposite, showing a significant negative correlation. Ash was less affected by temperature. Along with the rise of temperature, the thermal diffusivity of three coal samples decreased first but later increased, and the specific heat was always in a state of increasing. The change in thermal conductivity was mainly affected by specific heat. By calculating the gray correlation degree, the major factors affecting the thermophysical properties were obtained

Intrinsic Piezoelectric Anisotropy of Tetragonal ABO3 Perovskites: A High-Throughput Study

A comprehensive understand of the intrinsic piezoelectric anisotropy stemming from diverse chemical and physical factors is a key step for the rational design of highly anisotropic materials. We performed high-throughput calculations on tetragonal ABO3 perovskites to investigate the piezoelectricity and the interplay between lattice, displacement, polarization and elasticity. Among the 123 types of perovskites, the structural tetragonality is naturally divided into two categories: normal tetragonal (c/a ratio < 1.1) and super-tetragonal (c/a ratio > 1.17), exhibiting distinct ferroelectric, elastic, and piezoelectric properties. Charge analysis revealed the mechanisms underlying polarization saturation and piezoelectricity suppression in the super-tetragonal region, which also produces an inherent contradiction between high d33 and large piezoelectric anisotropy ratio |d33/d31|. The polarization axis and elastic softness direction jointly determine the maximum longitudinal piezoelectric response d33 direction. The validity and deficiencies of the widely utilized |d33/d31| ratio for representing piezoelectric anisotropy were reevaluated

Nanoscale hyperthermia mesostructures for sustainable antimicrobial design.

Sustainability is critical in addressing global challenges posed by prolonged pandemics that impact health, economies, and the environment. Here, we introduce a molecular engineering approach for thermoregulated antimicrobial management inspired by firewalking rituals. The study uses in situ spectroscopy and multi-scale modeling to validate a hierarchical design. Efficient light-to-thermal energy conversion is achieved by engineering the molecular band structure. Rapid nanoscale hyperthermia is facilitated through thermal engineering. This approach significantly reduces the half-life of pathogens such as Escherichia coli, influenza A, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to 1.4 min while maintaining a low perceived temperature on human skin. Standard disease infection and epidemic models show this technologys potential to flatten outbreak curves and delay peak infection rates, which is crucial during the early stages of pandemics when developing vaccines and antiviral drugs takes time. The scalable manufacturing and broad antimicrobial applicability hold great promise for controlling emerging infectious diseases and diverse bioprotective applications