Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic anxiety recognition method based on microblog text analysis

Mental health has traditionally been assessed using a self-report questionnaire. Although this approach produces accurate results, it has the disadvantage of being labor-intense and time-consuming. This study aimed to extract original text information published by users on the social media platform (Sina Weibo). A machine learning method was used to train the model and predict the anxiety state of the user automatically. Data of 1,039 users were collected. First, Weibo users were invited to fill the anxiety self-assessment scale. All original text data ever published by the users were collected. Second, the Simplified Chinese-Linguistic Inquiry and Word Count (SC-LIWC) were extracted for feature selection and model training. We found that the model achieved the best performance when the XGBoostRegressor algorithm was used. The Pearson correlation coefficient between the model predicted scores and self-reported scores was moderate (r = 0.322). In addition, we tested the reliability of the model, and found that the model had high reliability (r = 0.72). The experimental results further showed that the model was feasible and effective and could use the digital footprints to predict psychological characteristics.
Opportunities for CAR-T Cell Immunotherapy in HIV Cure

Chimeric antigen receptor (CAR) technology is having a huge impact in the blood malignancy field and is becoming a well-established therapy for many types of leukaemia. In recent decades, efforts have been made to demonstrate that CAR-T cells have potential as a therapy to achieve a sterilizing cure for human immunodeficiency virus (HIV) infection. However, translation of this technology to the HIV scenario has not been easy, as many challenges have appeared along the way that hinder the consolidation of CAR-T cells as a putative therapy. Here, we review the origin and development of CAR-T cells, describe the advantages of CAR-T cell therapy in comparison with other therapies, and describe the major obstacles currently faced regarding application of this technology in the HIV field, specifically, viral escape, CAR-T cell infectivity, and accessibility to hidden reservoirs. Nonetheless, promising results in successfully tackling some of these issues that have been obtained in clinical trials suggest a bright future for CAR-T cells as a consolidated therapy.
Bilateral lung transplantation for pediatric pulmonary arterial hypertension: perioperative management and one-year follow-up

Background Bilateral lung transplantation (LuTx) remains the only established treatment for children with end-stage pulmonary arterial hypertension (PAH). Although PAH is the second most common indication for LuTx, little is known about optimal perioperative management and midterm clinical outcomes. Methods Prospective observational study on consecutive children with PAH who underwent LuTx with scheduled postoperative VA-ECMO support at Hannover Medical School from December 2013 to June 2020. Results Twelve patients with PAH underwent LuTx (mean age 11.9 years; age range 1.9–17.8). Underlying diagnoses included idiopathic (n = 4) or heritable PAH (n = 4), PAH associated with congenital heart disease (n = 2), pulmonary veno-occlusive disease (n = 1), and pulmonary capillary hemangiomatosis (n = 1). The mean waiting time was 58.5 days (range 1–220d). Three patients were bridged to LuTx on VA-ECMO. Intraoperative VA-ECMO/cardiopulmonary bypass was applied and VA-ECMO was continued postoperatively in all patients (mean ECMO-duration 185 h; range 73–363 h; early extubation). The median postoperative ventilation time was 28 h (range 17–145 h). Echocardiographic conventional and strain analysis showed that 12 months after LuTx, all patients had normal biventricular systolic function. All PAH patients are alive 2 years after LuTx (median follow-up 53 months, range 26–104 months). Conclusion LuTx in children with end-stage PAH resulted in excellent midterm outcomes (100% survival 2 years post-LuTx). Postoperative VA-ECMO facilitates early extubation with rapid gain of allograft function and sustained biventricular reverse-remodeling and systolic function after RV pressure unloading and LV volume loading.
Network-Based and Machine-Learning Approaches Identify Diagnostic and Prognostic Models for EMT-Type Gastric Tumors

The microsatellite stable/epithelial-mesenchymal transition (MSS/EMT) subtype of gastric cancer represents a highly aggressive class of tumors associated with low rates of survival and considerably high probabilities of recurrence. In the era of precision medicine, the accurate and prompt diagnosis of tumors of this subtype is of vital importance. In this study, we used Weighted Gene Co-expression Network Analysis (WGCNA) to identify a differentially expressed co-expression module of mRNAs in EMT-type gastric tumors. Using network analysis and linear discriminant analysis, we identified mRNA motifs and microRNA-based models with strong prognostic and diagnostic relevance: three models comprised of (i) the microRNAs miR-199a-5p and miR-141-3p, (ii) EVC/EVC2/GLI3, and (iii) PDE2A/GUCY1A1/GUCY1B1 gene expression profiles distinguish EMT-type tumors from other gastric tumors with high accuracy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.995, AUC = 0.9742, and AUC = 0.9717; respectively). Additionally, the DMD/ITGA1/CAV1 motif was identified as the top motif with consistent relevance to prognosis (hazard ratio > 3). Molecular functions of the members of the identified models highlight the central roles of MAPK, Hh, and cGMP/cAMP signaling in the pathology of the EMT subtype of gastric cancer and underscore their potential utility in precision therapeutic approaches.
Effect of Sublethal Concentrations of Zinc Oxide Nanoparticles on Bacillus cereus

Zinc oxide nanoparticles (ZnONPs), which are produced on a large scale, pose a potential threat to various environments because they can interact with the microbial populations found in them. Bacteria that are widespread in soil, water, and plant material include the Bacillus cereus group, which plays an important role in biodegradation and the nutrient cycle and is a major factor determining ecological balance. This group includes, among others, the foodborne pathogen B. cereus sensu stricto (herein referred to as B. cereus). The aim of this study was a comprehensive assessment of the effects of commercially available ZnONPs on B. cereus. The MIC (minimum inhibitory concentration) for B. cereus was 1.6 mg/mL, and the MBC (minimum bactericidal concentration) was 1.8 mg/mL. Growth of B. cereus was inhibited by a concentration of ZnONPs lower than or equal to MIC50. Concentrations from 0.2 to 0.8 mg/mL inhibited the growth of these bacteria in liquid media, induced symptoms of oxidative stress, and stimulated an environmental stress response in the form of biofilm and endospore formation. In addition, ZnONPs negatively affected the ability of the bacteria to break down the azo dye Evans Blue but enhanced the antimicrobial properties of phenolic compounds. Sublethal concentrations of ZnONPs generally decreased the activity of B. cereus cells, especially in the presence of phenolics, which indicates their potential toxicological impact, but at the same time they induced universal defence responses in these cells, which in the case of potential pathogens can hinder their removal.
Parameterization and Impact of Ice initiation Processes Relevant to Numerical Model Simulations of Cirrus Clouds.

Abstract An effort to improve descriptions of ice initiation processes of relevance to cirrus clouds for use in regional-scale numerical cloud models with bulk microphysical schemes is described. This is approached by deriving practical parameterizations of the process of ice initiation by homogeneous freezing of cloud and haze (CCN) particles in the atmosphere. The homogeneous freezing formulations may be used with generalized distributions of cloud water and CCN (pure ammonium sulfate assumed). Numerical cloud model sensitivity experiments were made using a microphysical parcel model and a mososcale cloud model to investigate the impact of the homogeneous freezing process and heterogeneous ice nucleation processes on the formation and makeup of cirrus clouds. These studies point out the critical nature of assumptions made regarding the abundance and character of heterogeneous ice nuclei (IN) present in the upper troposphere. Conclusions regarding the sources of ice crystals in cirrus clouds and the pote.
On Gender and Apology: Case of Algerian Arabic

The present study explores the impact of gender on the use of apology strategies by Algerian speakers. Speciﬁcally, the research aims to identify the apology strategies utilized by male and female Algerian participants and to examine the potential inﬂuence of gender on such usage. The study employed a discourse completion task (DCT) as its methodological tool for data collection. The DCT included nine scenarios in which participants were required to provide an apology. Sixty participants, consisting of 30 males and 30 females, were recruited for the study. Both qualitative and quantitative analyses were employed to examine the data collected. To facilitate the analysis, a modiﬁed version of Blum-Kulka and Olshtain’s (1984) framework was applied, given its widespread usage and convenience in similar studies. The ﬁndings of the study indicate that male and female Algerian participants employed seven primary apology strategies, and gender inﬂuenced the frequency of their usage. These ﬁndings provide new insights into the role of gender in the choice of apology strategies in Algerian Arabic.
IRWoZ: Constructing an Industrial Robot Wizard-of-OZ Dialoguing Dataset

Enabling a flexible and natural human-robot interaction (HRI) for industrial robots is a critical yet challenging task that can be facilitated by the use of conversational artificial intelligence (AI). Prior research has concentrated on strengthening interactions through the deployment of social robots, while disregarding the capabilities required to boost the flexibility and user experience associated with human-robot collaboration (HRC) on manufacturing tasks. One of the main challenges is the lack of publicly available industrial-oriented dialogue datasets for the training of conversational AI. In this work, we present an Industrial Robot Wizard-of-Oz Dialoguing Dataset (IRWoZ) focused on enabling HRC in manufacturing tasks. The dataset covers four domains: assembly, transportation, position, and relocation. It is created using the Wizard-of-Oz technique to be less noisy. We manually constructed, annotated and validated dialogue segments (e.g., intentions, slots, annotations), as well as the responses. Building upon the proposed dataset, we benchmark it on the state-of-the-art (SoTA) language models, generative pre-trained (GPT-2) models, on dialogue state tracking and response generation tasks. We expect that the IRWoZ dataset will facilitate exciting ongoing dialogue research and we provide it freely accessible at https://github.com/lcroy/ToD4IR/tree/main/dataset.
Significant Association of Estrogen Receptor-β Isoforms and Coactivators in Breast Cancer Subtypes

Nuclear receptor coregulators are the principal regulators of Estrogen Receptor (ER)-mediated transcription. ERβ, an ER subtype first identified in 1996, is associated with poor outcomes in breast cancer (BCa) subtypes, and the coexpression of the ERβ1 isoform and AIB-1 and TIF-2 coactivators in BCa-associated myofibroblasts is associated with high-grade BCa. We aimed to identify the specific coactivators that are involved in the progression of ERβ-expressing BCa. ERβ isoforms, coactivators, and prognostic markers were tested using standard immunohistochemistry. AIB-1, TIF-2, NF-kB, p-c-Jun, and/or cyclin D1 were differentially correlated with ERβ isoform expression in the BCa subtypes and subgroups. The coexpression of the ERβ5 and/or ERβ1 isoforms and the coactivators were found to be correlated with a high expression of P53, Ki-67, and Her2/neu and large-sized and/or high-grade tumors in BCa. Our study supports the notion that ERβ isoforms and coactivators seemingly coregulate the proliferation and progression of BCa and may provide insight into the potential therapeutic uses of the coactivators in BCa.
Adaptive fuzzy sliding mode control of nonlinear system: the second control scheme

An adaptive sliding mode control scheme in which fuzzy logic systems (approximators) are used to approximate the unknown system functions of nonlinear systems is proposed. In order to reduce the approximation errors between the true nonlinear functions and fuzzy approximators, a robust adaptive law is also proposed. The stabilities of the proposed control schemes are proved. An inverted pendulum system is considered to verify the validities of the proposed schemes in simulation.
First Results of a New Deep Learning Reconstruction Algorithm on Image Quality and Liver Metastasis Conspicuity for Abdominal Low-Dose CT

The study’s aim was to assess the impact of a deep learning image reconstruction algorithm (Precise Image; DLR) on image quality and liver metastasis conspicuity compared with an iterative reconstruction algorithm (IR). This retrospective study included all consecutive patients with at least one liver metastasis having been diagnosed between December 2021 and February 2022. Images were reconstructed using level 4 of the IR algorithm (i4) and the Standard/Smooth/Smoother levels of the DLR algorithm. Mean attenuation and standard deviation were measured by placing the ROIs in the fat, muscle, healthy liver, and liver tumor. Two radiologists assessed the image noise and image smoothing, overall image quality, and lesion conspicuity using Likert scales. The study included 30 patients (mean age 70.4 ± 9.8 years, 17 men). The mean CTDIvol was 6.3 ± 2.1 mGy, and the mean dose-length product 314.7 ± 105.7 mGy.cm. Compared with i4, the HU values were similar in the DLR algorithm at all levels for all tissues studied. For each tissue, the image noise significantly decreased with DLR compared with i4 (p < 0.01) and significantly decreased from Standard to Smooth (−26 ± 10%; p < 0.01) and from Smooth to Smoother (−37 ± 8%; p < 0.01). The subjective image assessment confirmed that the image noise significantly decreased between i4 and DLR (p < 0.01) and from the Standard to Smoother levels (p < 0.01), but the opposite occurred for the image smoothing. The highest scores for overall image quality and conspicuity were found for the Smooth and Smoother levels.
Equatorial Jets in Decaying Shallow-Water Turbulence on a Rotating Sphere

Abstract Ensemble experiments of decaying shallow-water turbulence on a rotating sphere are performed to confirm the robustness of the emergence of an equatorial jet. While previous studies have reported that the equatorial jets emerging in shallow-water turbulence are always retrograde, predominance of a prograde jet, although less likely, was also found in the present ensemble experiments. Furthermore, a zonal-mean flow induced by wave–wave interactions was examined using a weak nonlinear model to investigate the acceleration mechanisms of the equatorial jet. The second-order acceleration is induced by the Rossby and mixed Rossby–gravity waves and its mechanisms can be categorized into two types. First, the local meridional wavenumber of a Rossby wave packet propagating toward the equator increases because of meridional variation of the Rossby deformation radius and/or the retrograde zonal-mean flow, resulting in a dissipation of the wave packet in the equatorial region. This mechanism always contribute.
A Combination of Deep-Sea Water and Fucoidan Alleviates T2DM through Modulation of Gut Microbiota and Metabolic Pathways

Fucoidan and deep-sea water (DSW) are attractive marine resources for treating type 2 diabetes (T2DM). In this study, the regulation and mechanism associated with the co-administration of the two were first studied using T2DM rats, induced by a high fat diet (HFD) and streptozocin (STZ) injection. Results demonstrate that, compared to those with DSW or FPS alone, the orally administered combination of DSW and FPS (CDF), especially the high dose (H-CDF), could preferably inhibit weight loss, decrease levels of fasting blood glucose (FBG) and lipids, and improve hepatopancreatic pathology and the abnormal Akt/GSK-3β signaling pathway. The fecal metabolomics data show that H-CDF could regulate the abnormal levels of metabolites mainly through the regulation of linoleic acid (LA) metabolism, bile acid (BA) metabolism, and other related pathways. Moreover, H-CDF could adjust the diversity and richness of bacterial flora and enrich bacterial groups, such as Lactobacillaceae and Ruminococcaceae UCG-014. In addition, Spearman correlation analysis illustrated that the interaction between the gut microbiota and BAs plays an essential role in the action of H-CDF. In the ileum, H-CDF was verified to inhibit activation of the farnesoid X receptor (FXR)–fibroblast growth factor 15 (FGF15) pathway, which is regulated by the microbiota–BA–axis. In conclusion, H-CDF enriched Lactobacillaceae and Ruminococcaceae UCG-014, thereby changing BA metabolism, linoleic acid metabolism, and other related pathways, as well as enhancing insulin sensitivity and improving glucose and lipid metabolism.
Resonant Fast-Alfvén Wave Coupling in a 3D Coronal Arcade

The resonant excitation of Alfvén waves using the fast magnetosonic mode is important in space plasmas. In this paper, we consider a simple model of a three-dimensional (3D) coronal arcade. A numerical approach is used to produce a driven normal mode. We find that resonant coupling can occur in 3D, but there are new features that are absent in 2D. In particular, the polarisation of the Alfvén waves can vary with position throughout the Resonant Zone. Moreover, there are an infinite number of possible paths the resonant waves can exist on.
Determination of a Subset from Certain Combinatorial Properties

Let N be a finite set of n elements. A collection ﹛S1, S2, … , Sm﹜ of subsets of N is called a determining collection if an arbitrary subset T of N is uniquely determined by the cardinalities of the intersections Si ⋂ T, 1 ≤ i ≤ m. The purpose of this paper is to study the minimum value D(n) of m for which a determining collection of m subsets exists. This problem can be expressed as a coin-weighing problem (1; 7). In a recent paper Cantor (1) showed that D(n) = O(n/log log n), thus proving a conjecture of N. J. Fine (3) that D(n) = o(n). More recently Erdös and Rényi (2), Söderberg and Shapiro (7), Berlekamp, Mills, and Leo Moser have independently found proofs that D(n) = O(n/log n).
Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images

Abstract. Purpose Accurate detection of small renal masses (SRM) is a fundamental step for automated classification of benign and malignant or indolent and aggressive renal tumors. Magnetic resonance image (MRI) may outperform computed tomography (CT) for SRM subtype differentiation due to improved tissue characterization, but is less explored compared to CT. The objective of this study is to autonomously detect SRM on contrast-enhanced magnetic resonance images (CE-MRI). Approach In this paper, we described a novel, fully automated methodology for accurate detection and localization of SRM on CE-MRI. We first determine the kidney boundaries using a U-Net convolutional neural network. We then search for SRM within the localized kidney regions using a mixture-of-experts ensemble model based on the U-Net architecture. Our dataset contained CE-MRI scans of 118 patients with different solid kidney tumor subtypes including renal cell carcinomas, oncocytomas, and fat-poor renal angiomyolipoma. We evaluated the proposed model on the entire CE-MRI dataset using 5-fold cross validation. Results The developed algorithm reported a Dice similarity coefficient of 91.20 ± 5.41 % (mean ± standard deviation) for kidney segmentation from 118 volumes consisting of 25,025 slices. Our proposed ensemble model for SRM detection yielded a recall and precision of 86.2% and 83.3% on the entire CE-MRI dataset, respectively. Conclusions We described a deep-learning-based method for fully automated SRM detection using CE-MR images, which has not been studied previously. The results are clinically important as SRM localization is a pre-step for fully automated diagnosis of SRM subtypes.
Path Planning for Large-Scale Data Collection in UAV-Assisted loT System

Unmanned aerial vehicle (UAV) is deemed the most prospective technique for data collection in the future Internet of Things (IoT), due to its flexible deployment, potential mobility, and low cost. In order to ensure that UAV s can choose the most efficient and reliable flight route in complex environments, we propose a novel path planning scheme for data collection in a large-scale loT system based on Hilbert curve and ge-netic algorithm (HC-GA). We aim to minimize the total path length of the UAV by optimizing the flight path of the UA V. Meanwhile, the simulation time of the proposed algorithm is optimized to ensure real-time. Simulation results demonstrate that the proposed scheme could reduce the path length efficiently compared with benchmark algorithms. The simulation time cost for path planning is limited to a satisfactory volume, especially for scenarios with a large scale of sensor nodes.
Proteomic Study on Multi-Organ Metastases of Human Ovarian Clear Cell Carcinoma Cell Line in a Xenograft Mouse Model Based on a Novel Sequence-Specific Analysis Strategy.

BACKGROUND To investigate the gene regulation of tumor cells in the process of different organ metastasis on a xenograft mouse model and screen the genes involved in the organ-target metastasis of tumor cells. METHODS A multi-organ metastasis model was constructed with a human ovarian clear cell carcinoma cell line (ES-2) based on a severe immunodeficiency mouse strain (NCG). Differentially expressed tumor proteins among multi-organ metastases were successfully characterized by microliter liquid chromatography-high-resolution mass spectrometry, sequence-specific data analysis and multivariate statistical data analysis. Liver metastases were selected as typical for subsequent bioinformatic analysis. Selected liver metastasis-specific genes in ES-2 cells were validated by sequence-specific quantitation including high resolution-multiple reaction monitoring quantification at protein level and quantitative real-time polymerase chain reaction at mRNA level. RESULTS From the mass spectrometry data, a total of 4503 human proteins were identified using the sequence-specific data analysis strategy. Of them, 158 proteins were selected as specifically regulated genes in liver metastases for subsequent bioinformatics studies. Based on Ingenuity Pathway Analysis (IPA) pathway analysis and sequence-specific quantitation, Ferritin light chain (FTL), lactate dehydrogenase A (LDHA) and long-chain-fatty-acid-CoA ligase 1 (ACSL1) were finally validated as specifically upregulated proteins in liver metastases. CONCLUSIONS Our work provides a new approach to analyze gene regulation in tumor metastasis in xenograft mouse model. In presence of a large number of mouse protein interference, we validated the up-regulation of human ACSL1, FTL and LDHA in ES-2 liver metastases, which reflects the adaptive regulation of tumor cells to the liver microenvironment through metabolic reprogramming.