A double-distribution-function lattice Boltzmann method for bed-load sediment transport

The governing equations of bed-load sediment transport are the shallow water equations and the Exner equation. To embody the advantages of the lattice Boltzmann method (e.g., simplicity, efficiency), the three-velocity (D1Q3) and five-velocity (D1Q5) double-distribution-function lattice Boltzmann models (DDF-LBMs), which can present the numerical solution for one-dimensional bed-load sediment transport, are proposed here based on the quasi-steady approach. The so-called DDF-LBM means we use two distribution functions to describe the movement of the two components, respectively. By using the Chapman–Enskog expansion, the governing equations can be recovered correctly from the DDF-LBMs. To illustrate the efficiency of these, two benchmark tests are used, and excellent agreements between the numerical and analytical solutions are demonstrated. In addition, we show that the D1Q5 DDF-LBM has better accuracy compared to the Hudson’s method

Effect of expanded polystyrene content and press temperature on the properties of low-density wood particleboard

In this study, three-layer low-density (about 400 kg/m3) particleboards consisting of a mixture of wood particles and expanded polystyrene (EPS) were manufactured. EPS bead was incorporated in the core layer as a light filler. The influence of EPS content (0 %, 2,5 %, 5 %, 7,5 %, 10 % and 12,5 %) and press temperature (110 °C and 140 °C) on the microstructure, density profile, bending properties, internal bond and thickness swelling of the panels was investigated. Results showed that incorporation of EPS beads filled in the voids between wood particles, improved the core layer integrity, and generated a more pronounced density profile. Consequently, the bending properties and internal bond of panels adding EPS were remarkably improved, and the thickness swelling was decreased. However, the variation of the amount of EPS from 2,5 % to 12,5 % had no significant effect on the bending properties and thickness swelling. Comparing the two press temperatures, although higher temperature (140 °C) was more favourable in control panels without EPS as filler, it had a negative effect on the properties of panels with addition of EPS filler, especially for high EPS contents (10 % and 12,5 %), attributing to the shrinkage of EPS bead under press temperature that is much higher than its glass transition temperature (104 °C)

IN VIVO IDENTIFICATION OF PERIODONTIUM MSCS AND THEIR RESPONSE TO PERIODONTITIS

Periodontium is the supporting tissue for teeth and is composed of alveolar bone, periodontal ligament (PDL), gingiva and cementum. Periodontal tissues are known to undergo constant turnover supported by the stem cell population. However, this process remain poorly understood because of the failure to identify and locate periodontium mesenchymal stem cells (MSCs) in vivo. Advanced periodontitis, which results in periodontium impairment (including alveolar bone loss and PDL space enlargement), is a major cause of tooth loss in adults. However, why MSCs fail to maintain periodontium integrity in infectious conditions is largely unknown. The goal of my thesis was to test a dual hypothesis: the transcription factor glioma-associated (Gli1) + cells surrounding the neurovascular bundle (NVB) inside PDL are periodontium MSCs, which play a critical role in periodontal tissue turnover and injury repair under the control of Wnt-ß-catenin signaling. Secondly, during periodontitis pathogenesis, the bioactivity of Gli1+ periodontium MSCs is compromised. To test the hypothesis, we used newly developed tissue clearing and multiple imaging techniques in Gli1-CreERT2; Ai14 transgenic fluorescent reporter mice line, plus the conditional knockout strain in combination with the ligation-periodontitis model. Our key findings were: 1). The newly developed tissue clearing method revealed a three-dimensional view of Gli1+ MSCs distribution in adult mouse molar periodontium for the first time. 2). The Gli1+ cells surrounding NVB are periodontium MSCs, which actively maintain periodontium integrity during the animal`s adult stage. Likewise, to facilitate this integrity, 3). Wnt signaling is essential in the regulation of Gli1+ MSCs. However, we also discovered that a loss of ß-catenin within the Gli1+ MSCs (Gli1-CreERT2; ßcatenin loxP; Ai14 line) leads to severe periodontal tissue defects. For example, 4). overactivated Wnt signaling within Gli1+ MSCs (Gli1-CreERT2; ß-catenin-(Exon3) loxP; Ai14 line) leads to periodontium overgrowth in vivo. 5). Periodontitis also inhibits Gli1+ MSC' activation and lineage commitment activity. 6). Accordingly, chronic periodontitis compromises Gli1+ MSC maintenance. In addition to the issues above, PDL vasculature is compromised in advanced periodontitis. 8). Lastly, we found out that Wnt signaling activity is downregulated during periodontitis while 9) over-activation of Wnt signaling within Gli1+ MSCs restores normal periodontal morphology. Overall, our study found a reliable in vivo marker to label adult mouse molar periodontium MSCs and successfully localized them for the first time. Therefore, our work provides an effective animal model to further study the in vivo response of periodontal MSCs on pathological conditions, thereby providing insight for treatment planning in dental clinics. We further demonstrated the impact of infectious periodontitis on Gli1+ MSCs and revealed part of the mechanism behind the persistence of advanced periodontitis

How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning

Our investigation into the Affective Reasoning in Conversation (ARC) task highlights the challenge of causal discrimination. Almost all existing models, including large language models (LLMs), excel at capturing semantic correlations within utterance embeddings but fall short in determining the specific causal relationships. To overcome this limitation, we propose the incorporation of \textit{i.i.d.} noise terms into the conversation process, thereby constructing a structural causal model (SCM). It explores how distinct causal relationships of fitted embeddings can be discerned through independent conditions. To facilitate the implementation of deep learning, we introduce the cogn frameworks to handle unstructured conversation data, and employ an autoencoder architecture to regard the unobservable noise as learnable "implicit causes." Moreover, we curate a synthetic dataset that includes i.i.d. noise. Through comprehensive experiments, we validate the effectiveness and interpretability of our approach. Our code is available in https://github.com/Zodiark-ch/mater-of-our-EMNLP2023-paper.Comment: accepted via EMNLP2023-mai

Marginal Structural Models with Counterfactual Effect Modifiers

In health and social sciences, research questions often involve systematic assessment of the modification of treatment causal effect by patient characteristics, in longitudinal settings with time-varying or post-intervention effect modifiers of interest. In this work, we investigate the robust and efficient estimation of the so-called Counterfactual-History-Adjusted Marginal Structural Model (van der Laan and Petersen (2007)), which models the conditional intervention-specific mean outcome given modifier history in an ideal experiment where, possible contrary to fact, the subject was assigned the intervention of interest, including the treatment sequence in the conditioning history. We establish the semiparametric efficiency theory for these models, and present a substitution-based, semiparametric efficient and doubly robust estimator using the targeted maximum likelihood estimation methodology (TMLE, e.g. van der Laan and Rubin (2006), van der Laan and Rose (2011)). To facilitate implementation in applications where the effect modifier is high dimensional, our third contribution is a projected influence curve (and the corresponding TMLE estimator), which retains most of the robustness of its efficient peer and can be easily implemented in applications where the use of the efficient influence curve becomes taxing. In addition to these two robust estimators, we also present an Inverse-Probability-Weighted (IPW) estimator (e.g. Robins (1997a), Hernan, Brumback, and Robins (2000)), and a non-targeted G-computation estimator (Robins (1986)). The comparative performance of these estimators are assessed in a simulation study. The use of the TMLE estimator (based on the projected influence curve) is illustrated in a secondary data analysis for the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial

Detection-driven exposure-correction network for nighttime drone-view object detection.

Drone-view object detection (DroneDet) models typically suffer a significant performance drop when applied to nighttime scenes. Existing solutions attempt to employ an exposure-adjustment module to reveal objects hidden in dark regions before detection. However, most exposure-adjustment models are only optimized for human perception, where the exposure-adjusted images may not necessarily enhance recognition. To tackle this issue, we propose a novel Detection-driven Exposure-Correction network for nighttime DroneDet, called DEDet. The DEDet conducts adaptive, non-linear adjustment of pixel values in a spatially fine-grained manner to generate DroneDet-friendly images. Specifically, we develop a Fine-grained Parameter Predictor (FPP) to estimate pixel-wise parameter maps of the image filters. These filters, along with the estimated parameters, are used to adjust pixel values of the low-light image based on non-uniform illuminations in drone-captured images. In order to learn the non-linear transformation from the original nighttime images to their DroneDet-friendly counterparts, we propose a Progressive Filtering module that applies recursive filters to iteratively refine the exposed image. Furthermore, to evaluate the performance of the proposed DEDet, we have built a dataset NightDrone to address the scarcity of the datasets specifically tailored for this purpose. Extensive experiments conducted on four nighttime datasets show that DEDet achieves a superior accuracy compared with the state-of-the-art methods. Furthermore, ablation studies and visualizations demonstrate the validity and interpretability of our approach. Our NightDrone dataset can be downloaded from https://github.com/yuexiemail/NightDrone-Dataset

Detection of Brucellae in peripheral blood mononuclear cells for monitoring therapeutic efficacy of brucellosis infection.

BACKGROUND: Brucellosis is one of the most severe widespread zoonoses caused by the Gram-negative bacterium Brucella species. The diagnosis and clinical assessment of human brucellosis are very important for the management of patients, while there is a lack of effective methods to detect Brucellae. Classical culture of Brucella species is time consuming and often fails. A simple and sensitive assay is needed for diagnosis of Brucella infection and monitoring of treatment in man. METHODS: Blood samples and peripheral blood mononuclear cells (PBMCs) were collected from 154 patients hospitalized for brucellosis. Brucella antibodies were detected by Rose Bengal Plate Test (RBPT), Standard Tube Agglutination Test (SAT) and enzyme-linked immunosorbent assay (ELISA). Intracellular Brucellae were detected by blood culture and immunofluorescence staining (IFS). RESULTS: Among 154 brucellosis patients, 59.7% (92/154) were antibody reactive by RBPT, 81.8% (126/154) by SAT and 95.5% (147/154) by ELISA, respectively. Only 3.2% (5/154) of patient blood samples resulted in positive Brucella culture, while 68.8% (106/154) carried IFS detectable Brucella antigens in PBMCs. Gender (P = 0.01) but not age (P > 0.05) was a significant risk factor. The frequency of intracellular Brucella antigens was similar between patients receiving different treatment regimens (P > 0.05). However, a significant decrease of intracellular Brucellae was observed only in patients with acute brucellosis after the third course of treatment (P < 0.05), suggesting that current regimens to treat chronic brucellosis were not effective. CONCLUSIONS: IFS appears a sensitive assay for detection of Brucella antigens in PBMCs and could be used for diagnosis and therapeutic monitoring of brucellosis in clinical practice

Marmoset Viral Hepatic Inflammation Induced by Hepatitis C Virus Core Protein via IL-32.

Common marmosets infected with GB virus-B (GBV-B) chimeras containing hepatitis C virus (HCV) core and envelope proteins (CE1E2p7) developed more severe hepatitis than those infected with HCV envelope proteins (E1E2p7), suggesting that HCV core protein might be involved in the pathogenesis of viral hepatitis. The potential role of HCV core in hepatic inflammation was investigated. Six individual cDNA libraries of liver tissues from HCV CE1E2p7 or E1E2p7 chimera-infected marmosets (three animals per group) were constructed and sequenced. By differential expression gene analysis, 30 of 632 mRNA transcripts were correlated with the immune system process, which might be associated with hepatitis. A protein-protein interaction network was constituted by STRING database based on these 30 differentially expressed genes (DEGs), showing that IL-32 might play a central regulatory role in HCV core-related hepatitis. To investigate the effect of HCV core protein on IL-32 production, HCV core expressing and mock constructs were transfected into Huh7 cells. IL-32 mRNA and secretion protein were detected at significantly higher levels in cells expressing HCV core protein than in those without HCV core expression (P < 0.01 and P < 0.001, respectively). By KEGG enrichment analysis and using the specific signaling pathway inhibitor LY294002 for inhibition of PI3K, IL-32 expression was significantly reduced (P < 0.001). In conclusion, HCV core protein induces an increase of IL-32 expression via the PI3K pathway in hepatic cells, which played a major role in development of HCV-related severe hepatitis

The relationship between fundamental motor skills and physical fitness in preschoolers: a short-term longitudinal study

PurposePhysical fitness and fundamental motor skills are two important aspects for the healthy development of preschoolers. Despite the growing interest in clarifying their relationship, the scarcity of longitudinal studies prevents us from understanding causality.MethodThis study employed a cross-lagged model with two time points to investigate the bidirectional relationship between these two aspects. A total of 174 preschoolers (54.0% girls) from 3 to 6 years old (M = 3.96 ± 0.47) were surveyed, they were recruited by convenience from two kindergartens in Beijing, China, and their physical fitness (via CNPFDSM-EC) and fundamental motor skills (via TGMD-3) were tracked over a period of 6 months.ResultsThe findings revealed a bidirectional predictive effect. The predictive strength of flexibility was found to be lower than other physical fitness aspects, while locomotor skills demonstrated a higher predictive strength than object control skills.ConclusionThis study indicates that physical fitness and fundamental motor skills mutually enhance each other in young children, and both should be emphasized in preschool sports education