Reduced vowel space in video conferences via Zoom: Evidence from read speecha).

This exploratory study compared vowel space area (VSA) in face-to-face situations and video conference situations using the software Zoom. Twenty native German participants read word lists recorded before and after spontaneous conversation. The overall VSA in Zoom was reduced significantly by 11.9%, with a more reduced VSA before and less reduction after the spontaneous conversation. Of nine peripheral vowels in German, /aː iː yː/ showed a significantly reduced Euclidean distance to the centroid of the vowel space. The observed hypoarticulation is discussed in light of the experimental setup, situational differences, and less involvement in Zoom than in face-to-face situations.

Highly restricted near-surface permafrost extent during the mid-Pliocene warm period.

Accurate understanding of permafrost dynamics is critical for evaluating and mitigating impacts that may arise as permafrost degrades in the future; however, existing projections have large uncertainties. Studies of how permafrost responded historically during Earth's past warm periods are helpful in exploring potential future permafrost behavior and to evaluate the uncertainty of future permafrost change projections. Here, we combine a surface frost index model with outputs from the second phase of the Pliocene Model Intercomparison Project to simulate the near-surface (~3 to 4 m depth) permafrost state in the Northern Hemisphere during the mid-Pliocene warm period (mPWP, ~3.264 to 3.025 Ma). This period shares similarities with the projected future climate. Constrained by proxy-based surface air temperature records, our simulations demonstrate that near-surface permafrost was highly spatially restricted during the mPWP and was 93 ± 3% smaller than the preindustrial extent. Near-surface permafrost was present only in the eastern Siberian uplands, Canadian high Arctic Archipelago, and northernmost Greenland. The simulations are similar to near-surface permafrost changes projected for the end of this century under the SSP5-8.5 scenario and provide a perspective on the potential permafrost behavior that may be expected in a warmer world.

Thin Solid Polymer Electrolyte with High-Strength and Thermal-Resistant via Incorporating Nanofibrous Polyimide Framework for Stable Lithium Batteries.

Polyethylene oxide (PEO) based polymer electrolytes show promise in expanding the practical applications of lithium (Li) batteries. However, their applications in Li batteries are usually restricted owing to the lack of mechanical strength, poor oxidative stability, and relatively large thickness. Herein, a nanofibrous polyimide (PI) framework enhanced plasticized-PEO solid electrolyte is prepared to realize good mechanical and electrochemical performances. Following the configuration with the PI matrix, this "polymer in polymer" composite electrolyte with a thickness of 17.5 µm exhibits enhanced mechanical strength (13.9 MPa) and outstanding thermal stability. Additionally, it preserves the high ionic conductivity (2.25 × 10-4  S cm-1 , 25 °C). The Li||Li symmetrical battery with the modified electrolyte could achieve a steady Li plating/stripping of more than 500 h, and the critical current density reaches up to 0.6 mA cm-2 at ambient temperature. The LiFePO4 batteries delivery favorable capacity of 132.2 mAh g-1 with capacity retentions of 96.4% and 85.9% after 500 and 1000 cycles at 1 C, respectively. Acceptable cycling performance also could be achieved in LiNi0.5 Co0. 2 Mn0. 3 O2 solid batteries via an inorganic-rich artificial cathode electrolyte interphase.

Two-stage generative adversarial networks for metal artifact reduction and visualization in ablation therapy of liver tumors.

PURPOSE: The strong metal artifacts produced by the electrode needle cause poor image quality, thus preventing physicians from observing the surgical situation during the puncture process. To address this issue, we propose a metal artifact reduction and visualization framework for CT-guided ablation therapy of liver tumors. METHODS: Our framework contains a metal artifact reduction model and an ablation therapy visualization model. A two-stage generative adversarial network is proposed to reduce the metal artifacts of intraoperative CT images and avoid image blurring. To visualize the puncture process, the axis and tip of the needle are localized, and then the needle is rebuilt in 3D space intraoperatively. RESULTS: Experiments show that our proposed metal artifact reduction method achieves higher SSIM (0.891) and PSNR (26.920) values than the state-of-the-art methods. The accuracy of ablation needle reconstruction is 2.76 mm average in needle tip localization and 1.64° average in needle axis localization. CONCLUSION: We propose a novel metal artifact reduction and an ablation therapy visualization framework for CT-guided ablation therapy of liver cancer. The experiment results indicate that our approach can reduce metal artifacts and improve image quality. Furthermore, our proposed method demonstrates the potential for displaying the relative position of the tumor and the needle intraoperatively.

Study on the Thermogravimetric Kinetics of Dehydrated Sewage Sludge Regulated by Cationic Polyacrylamide and Sawdust.

With the continuous increase in sewage-sludge production worldwide, the pyrolytic disposal of sludge has received great attention. To build knowledge on the kinetics of pyrolysis, first, sludge was regulated using appropriate amounts of cationic polyacrylamide (CPAM) and sawdust to study their enhancing effect on dehydration. Due to the effects of the charge neutralization and skeleton hydrophobicity, a certain dose of CPAM and sawdust reduced the sludge's moisture content from 80.3% to 65.7%. Next, the pyrolysis characteristics of the dehydrated sludge regulated by CPAM and sawdust were investigated at a heating rate of 10~40 °C/min by using TGA method. The addition of sawdust enhanced the release of volatile substances and reduced the apparent activation energy of the sample. The maximum weight-loss rate decreased with the heating rate, and the DTG curves moved in the direction of high temperature. A model-free method, namely the Starink method, was adopted to calculate the apparent activation energies, which ranged from 135.3 kJ/mol to 174.8 kJ/mol. Combined with the master-plots method, the most appropriate mechanism function ultimately obtained was the nucleation-and-growth model.

Survival Analysis for Multimode Ablation Using Self-Adapted Deep Learning Network Based on Multisource Features.

Novel multimode thermal therapy by freezing before radio-frequency heating has achieved a desirable therapeutic effect in liver cancer. Compared with surgical resection, ablation treatment has a relatively high risk of tumor recurrence. To monitor tumor progression after ablation, we developed a novel survival analysis framework for survival prediction and efficacy assessment. We extracted preoperative and postoperative MRI radiomics features and vision transformer-based deep learning features. We also combined the immune features extracted from peripheral blood immune responses using flow cytometry and routine blood tests before and after treatment. We selected features using random survival forest and improved the deep Cox mixture (DCM) for survival analysis. To properly accommodate multitype input features, we proposed a self-adapted fully connected layer for locally and globally representing features. We evaluated the method using our clinical dataset. Of note, the immune features rank the highest feature importance and contribute significantly to the prediction accuracy. The results showed a promising C td-index of 0.885 ±0.040 and an integrated Brier score of 0.041 ±0.014, which outperformed state-of-the-art method combinations of survival prediction. For each patient, individual survival probability was accurately predicted over time, which provided clinicians with trustable prognosis suggestions.

Automatic vertebral fracture and three-column injury diagnosis with fracture visualization by a multi-scale attention-guided network.

Deep learning methods have the potential to improve the efficiency of diagnosis for vertebral fractures with computed tomography (CT) images. Most existing intelligent vertebral fracture diagnosis methods only provide dichotomized results at a patient level. However, a fine-grained and more nuanced outcome is clinically needed. This study proposed a novel network, a multi-scale attention-guided network (MAGNet), to diagnose vertebral fractures and three-column injuries with fracture visualization at a vertebra level. By imposing attention constraints through a disease attention map (DAM), a fusion of multi-scale spatial attention maps, the MAGNet can get task highly relevant features and localize fractures. A total of 989 vertebrae were studied here. After four-fold cross-validation, the area under the ROC curve (AUC) of our model for vertebral fracture dichotomized diagnosis and three-column injury diagnosis was 0.884 ± 0.015 and 0.920 ± 0.104, respectively. The overall performance of our model outperformed classical classification models, attention models, visual explanation methods, and attention-guided methods based on class activation mapping. Our work can promote the clinical application of deep learning to diagnose vertebral fractures and provide a way to visualize and improve the diagnosis results with attention constraints.

Computed tomography-based radiomics prediction of CTLA4 expression and prognosis in clear cell renal cell carcinoma.

OBJECTIVES: To predict CTLA4 expression levels and prognosis of clear cell renal cell carcinoma (ccRCC) by constructing a computed tomography-based radiomics model and establishing a nomogram using clinicopathologic factors. METHODS: The clinicopathologic parameters and genomic data were extracted from 493 ccRCC cases of the Cancer Genome Atlas (TCGA)-KIRC database. Univariate and multivariate Cox regression and Kaplan-Meier analysis were performed for prognosis analysis. Cibersortx was applied to evaluate the immune cell composition. Radiomic features were extracted from the TCGA/the Cancer Imaging Archive (TCIA) (n = 102) datasets. The support vector machine (SVM) was employed to establish the radiomics signature for predicting CTLA4 expression. Receiver operating characteristic curve (ROC), decision curve analysis (DCA), and precision-recall curve were utilized to assess the predictive performance of the radiomics signature. Correlations between radiomics score (RS) and selected features were also evaluated. An RS-based nomogram was constructed to predict prognosis. RESULTS: CTLA4 was significantly overexpressed in ccRCC tissues and was related to lower overall survival. A higher CTLA4 expression was independently linked to the poor prognosis (HR = 1.458, 95% CI 1.13-1.881, p = 0.004). The radiomics model for the prediction of CTLA4 expression levels (AUC = 0.769 in the training set, AUC = 0.724 in the validation set) was established using seven radiomic features. A significant elevation in infiltrating M2 macrophages was observed in the RS high group (p < 0.001). The predictive efficiencies of the RS-based nomogram measured by AUC were 0.826 at 12 months, 0.805 at 36 months, and 0.76 at 60 months. CONCLUSIONS: CTLA4 mRNA expression status in ccRCC could be predicted noninvasively using a radiomics model based on nephrographic phase contrast-enhanced CT images. The nomogram established by combining RS and clinicopathologic factors could predict overall survival for ccRCC patients. Our findings may help stratify prognosis of ccRCC patients and identify those who may respond best to ICI-based treatments.

Separation and Structural Characterization of a Novel Exopolysaccharide from Rhizopus nigricans.

The present study aims to analyze the structural characterization and antioxidant activity of a novel exopolysaccharide from Rhizopus nigricans (EPS2-1). For this purpose, EPS2-1 was purified through DEAE-52, Sephadex G-100, and Sephadex G-75 chromatography. The structural characterization of EPS2-1 was analyzed using high-performance gel permeation chromatography (HPGPC), Fourier transform infrared spectroscopy (FT-IR), methylation analysis, nuclear magnetic resonance (NMR) spectra, transmission electron microscope (TEM), and atomic force microscope (AFM). The results revealed that EPS2-1 is composed of mannose (Man), galactose (Gal), glucose (Glc), arabinose (Ara), and Fucose (Fuc), and possesses a molecular weight of 32.803 kDa. The backbone of EPS2-1 comprised →2)-α-D-Manp-(1→ and →3)-ß-D-Galp-(1→, linked with the O-6 position of (→2,6)-α-D-Manp-(1→) of the main chain is branch α-D-Manp-(1→6)-α-D-Manp-(1→, linked with the O-6 positions of (→3)-ß-D-Galp-(1→) of the main chain are branches →4)-ß-D-Glcp-(1→ and →3)-ß-D-Galp-(1→, respectively. Finally, we demonstrated that EPS2-1 also shows free radical scavenging activity and iron ion reducing ability. At the same time, EPS2-1 could inhibit the proliferation of MFC cells and increase the cell viability of RAW264.7 cells. Our results suggested that EPS2-1 is a novel polysaccharide, and EPS2-1 has antioxidant activity. In addition, EPS2-1 may possess potential immunomodulatory and antitumor activities. This study promoted the application of EPS2-1 as the functional ingredients in the pharmaceutical and food industries.

Aggregation caused quenching to aggregation induced emission transformation: a precise tuning based on BN-doped polycyclic aromatic hydrocarbons toward subcellular organelle specific imaging.

Polycyclic aromatic hydrocarbons (PAHs) with boron-nitrogen (BN) moieties have attracted tremendous interest due to their intriguing electronic and optoelectronic properties. However, most of the BN-fused π-systems reported to date are difficult to modify and exhibit traditional aggregation-caused quenching (ACQ) characteristics. This phenomenon greatly limits their scope of application. Thus, continuing efforts to seek novel, structurally distinct and functionally diverse structures are highly desirable. Herein, we proposed a one-stone-two-birds strategy including simultaneous exploration of reactivity and tuning of the optical and electronic properties for BN-containing π-skeletons through flexible regioselective functionalization engineering. In this way, three novel functionalized BN luminogens (DPA-BN-BFT, MeO-DPA-BN-BFT and DMA-DPA-BN-BFT) with similar structures were obtained. Intriguingly, DPA-BN-BFT, MeO-DPA-BN-BFT and DMA-DPA-BN-BFT exhibit completely different emission behaviors. Fluorogens DPA-BN-BFT and MeO-DPA-BN-BFT exhibit a typical ACQ effect; in sharp contrast, DMA-DPA-BN-BFT possesses a prominent aggregation induced emission (AIE) effect. To the best of our knowledge, this is the first report to integrate ACQ and AIE properties into one BN aromatic backbone with subtle modified structures. Comprehensive analysis of the crystal structure and theoretical calculations reveal that relatively large twisting angles, multiple intermolecular interactions and tight crystal packing modes endow DMA-DPA-BN-BFT with strong AIE behavior. More importantly, cell imaging demonstrated that luminescent materials DPA-BN-BFT and DMA-DPA-BN-BFT can highly selectively and sensitively detect lipid droplets (LDs) in living MCF-7 cells. Overall, this work provides a new viewpoint of the rational design and synthesis of advanced BN-polycyclic aromatics with AIE features and triggers the discovery of new functions and properties of azaborine chemistry.

A Novel Tumor Progression Prediction Method for Multimode Ablation Treatment.

OBJECTIVE: The multimode ablation of liver cancer, which uses radio-frequency heating after a pre-freezing process to treat the tumor, has shown significantly improved therapeutic effects and enhanced anti-tumor immune response. Unlike open surgery, the ablated lesions remain in the body after treatment, so it is critical to assess the immediate outcome and to monitor disease status over time. Here we propose a novel tumor progression prediction method for simultaneous postoperative evaluation and prognosis analysis. METHODS: We propose to leverage the intraoperative therapeutic information extracted from thermal dose distribution. For tumors with specific sensitivity reflected in medical images, different thermal doses implicitly indicate the degree of instant damage and long-term inhibition excited under specific ablation energy. We further propose a survival analysis framework for the multimode ablation treatment. It extracts carefully designed features from clinical, preoperative, intraoperative, and postoperative data, then uses random survival forest for feature selection and deep neural networks for survival prediction. RESULTS: We evaluated the proposed methods using clinical data. The results show that our method outperforms the state-of-the-art survival analysis methods with a C-index of 0.855±0.090. The thermal dose information contributes significantly to the prediction accuracy by taking up 21.7% of the overall feature importance. CONCLUSION: The proposed methods have been demonstrated to be a powerful tool in tumor progression prediction of multimode ablation therapy. SIGNIFICANCE: This kind of data-driven prognosis analysis may benefit personalized medicine and simplify the follow-up process.

A CT reconstruction method based on constrained data fidelity range estimation.

For the CT iterative reconstruction, choosing the parameters of different regularization terms has been a difficult problem. Transforming the reconstruction problem into constrained optimization can solve this problem, but determining the constraint range and accurately solving it remains a challenge. This paper proposes a CT reconstruction method based on constrained data fidelity term, which estimates the distribution of the constraint function by Taylor expansion to determine the constraint range. We respectively use Douglas-Rachford splitting (DRS) and Projection-based primal-dual algorithm (PPD) to split the reconstruction problem and solve the data fidelity subproblem. This method can accurately estimate the constrained range of data fidelity terms to ensure reconstruction accuracy and use different regularization terms for reconstruction without parameter adjustment. Three regularization terms are used for reconstruction experiments, and simulation results show that the proposed method can converge stably, and its reconstruction quality is better than the filtered back-projection.

Lung cancer subtype classification using histopathological images based on weakly supervised multi-instance learning.

Objective.Subtype classification plays a guiding role in the clinical diagnosis and treatment of non-small-cell lung cancer (NSCLC). However, due to the gigapixel of whole slide images (WSIs) and the absence of definitive morphological features, most automatic subtype classification methods for NSCLC require manually delineating the regions of interest (ROIs) on WSIs.Approach.In this paper, a weakly supervised framework is proposed for accurate subtype classification while freeing pathologists from pixel-level annotation. With respect to the characteristics of histopathological images, we design a two-stage structure with ROI localization and subtype classification. We first develop a method called multi-resolution expectation-maximization convolutional neural network (MR-EM-CNN) to locate ROIs for subsequent subtype classification. The EM algorithm is introduced to select the discriminative image patches for training a patch-wise network, with only WSI-wise labels available. A multi-resolution mechanism is designed for fine localization, similar to the coarse-to-fine process of manual pathological analysis. In the second stage, we build a novel hierarchical attention multi-scale network (HMS) for subtype classification. HMS can capture multi-scale features flexibly driven by the attention module and implement hierarchical features interaction.Results.Experimental results on the 1002-patient Cancer Genome Atlas dataset achieved an AUC of 0.9602 in the ROI localization and an AUC of 0.9671 for subtype classification.Significance.The proposed method shows superiority compared with other algorithms in the subtype classification of NSCLC. The proposed framework can also be extended to other classification tasks with WSIs.

Local changes in snow depth dominate the evolving pattern of elevation-dependent warming on the Tibetan Plateau.

Elevation-dependent warming (EDW), whereby warming rates are stratified by elevation, may increase the threat to the life-supporting solid water reservoir on the Tibetan Plateau. Previous studies have debated whether EDW exists and how it is driven. Using temperatures at 133 weather stations on the Tibetan Plateau during 17 different periods generated using a 30-year sliding window over 1973-2018, this study finds that the existence of EDW varies as the period moves forward, and critically it has become more severe over time. During the early part of the record with weaker regional warming, there were limited changes in snow depth and no EDW, but as time advances and regional warming intensifies, snow depth declines significantly at higher elevations, causing development of EDW. We conclude that enhanced regional warming has caused decreases in snow depth, largely controlling the pattern of EDW on the Tibetan Plateau. This may explain contrasting conclusions on EDW from previous studies which have used data for different periods, and our findings support enhanced EDW and more severe depletion of the Tibetan Plateau solid water reserves in a warmer future.

A Weak Supervision-based Framework for Automatic Lung Cancer Classification on Whole Slide Image.

Classification of normal lung tissue, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by pathological images is significant for clinical diagnosis and treatment. Due to the large scale of pathological images and the absence of definitive morphological features between LUAD and LUSC, it is time-consuming, laborious and challenging for pathologists to analyze the microscopic histopathology slides by visual observation. In this paper, a pixel-level annotation-free framework was proposed to classify normal tissue, LUAD and LUSC slides. This framework can be divided into two stages: tumor classification and localization, and subtype classification. In the first stage, EM-CNN was utilized to distinguish tumor slides from normal tissue slides and locate the discriminative regions for subsequent analysis with only image-level labels provided. In the second stage, a multi-scale network was proposed to improve the accuracy of subtype classification. This method achieved an AUC of 0.9978 for tumor classification and an AUC of 0.9684 for subtype classification, showing its superiority in lung pathological image classification compared with other methods.

SLIR: Synthesis, localization, inpainting, and registration for image-guided thermal ablation of liver tumors.

Thermal ablation is a minimally invasive procedure for treating small or unresectable tumors. Although CT is widely used for guiding ablation procedures, yet the contrast of tumors against normal soft tissues is often poor in CT scans, aggravating the accurate thermal ablation. In this paper, we propose a fast MR-CT image registration method to overlay pre-procedural MR (pMR) and pre-procedural CT (pCT) images onto an intra-procedural CT (iCT) image to guide the thermal ablation of liver tumors. At the pre-procedural stage, the Cycle-GAN model with mutual information constraint is employed to generate the synthesized CT (sCT) image from the input pMR. Then, pMR-pCT image registration is carried out via traditional mono-modal sCT-pCT image registration. At the intra-procedural stage, the region of the probe and its artifacts are automatically localized and inpainted in the iCT image. Then, an unsupervised registration network (UR-Net) is used to efficiently align the pCT with the inpainted iCT (inpCT) image. The final transform from pMR to iCT is obtained by concatenating the two estimated transforms, i.e., (i) from pMR image space to pCT image space (via sCT) and (ii) from pCT image space to iCT image space (via inpCT). The proposed method has been evaluated over a real clinical dataset and compared with state-of-the-art methods. Experimental results confirm that the proposed method achieves high registration accuracy with fast computation speed.

A squaraine-based fluorescence turn on chemosensor with ICT character for highly selective and sensitive detection of Al3+ in aqueous media and its application in living cell imaging.

A novel and simple squaraine-based fluorescent chemosensor SQ-BH bearing the benzoylhydrazine moiety was developed for the highly sensitive and selective detection of Al3+ in methanol-water mixture. The chemosensing behaviors of SQ-BH and its binding interaction with Al3+ were explored by various spectroscopic analyses. The reversibility of Al3+ recognition process was investigated using EDTA. The results of experiments and DFT/TDDFT calculations revealed that the chemosensor SQ-BH obeyed a turn on mechanism which was associated with the inhibited photoinduced electron transfer (PET), the enhanced intramolecular charge transfer (ICT) and the activated chelation enhanced fluorescence (CHEF). Furthermore, the fluorescent chemosensor SQ-BH whose excellent biocompatibility was confirmed by a standard MTT assay could be used to detect Al3+ in living cells, indicating its potential application value in biological fields.

Excited state intramolecular single proton transfer mechanism of pigment yellow 101 in solid state: Experiment and DFT calculation.

To investigate fluorescence mechanism of Pigment Yellow 101 (P. Y. 101) in solid state, three aromatic aldehyde azines (1-3) including P. Y. 101 have been synthesized and compared with each other. Results indicated that P. Y. 101 prepared by solvothermal method is actually the mixture of two polymorphs, whose molecular packing mode can be transformed into each other by recrystallizing or external stimuli such as pressure and grinding. The ESIPT properties of 1-3 were investigated by DFT/TD-DFT calculations and time-correlated single photon counting (TCSPC) technique. Both experimental and theoretical results revealed that the dual fluorescence properties of P. Y. 101 in solid state are ascribed to the excited-state intramolecular single proton transfer fluorescence emissions of two structurally different polymorphs rather than the results of the sequential or concerted excited-state intramolecular double proton transfers, which provide a potential valuable tool for developing multistimuli-responsive luminescent materials.