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In this work, the influence of Cu powders doping on the magnetic and mechanical properties of Sm2Co17 permanent magnets is systematically investigated. For high-energy-product Sm2Co17 (low initial Cu content), the intrinsic coercivity is significantly improved from 11.7 to 25.7 kOe with 1 wt.% Cu powders doping. The flux loss (soaking at 300 °C for 2 h) is reduced from 7.4% to 3.8%, and the average flexure strength is improved from 60.2 to 74.1 MPa. For high-temperature-resistant Sm2Co17 (high initial Cu content), the intrinsic coercivity is slightly reduced from 39.2 to 38.3 kOe with 1 wt.% Cu powders doping. The flux loss (soaking at 300 °C for 2 h) is increased from 1.4% to 3.4%, and the average flexure strength is improved from 86.7 to 93.2 MPa. The differences in magnetic properties change of these two Sm2Co17 permanent magnets are closely related with their initial Cu content. The doped Cu is prone to diffuse into intragranular cell boundaries in magnet with low initial Cu content. The increased Cu content in intragranular cell boundaries benefits the improvement of intrinsic coercivity, thus enhancing the thermal stability and reducing the flux loss. Doping Cu powders may increase the Cu-rich phase around ground boundaries, which will hinder the grain growth during heat treatment. The decrease of grain size will enhance the flexure strength, while it will increase the flux loss.

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Background: Hypokalemia has been associated with an increased risk of peritoneal dialysis (PD)-associated peritonitis. However, hypokalemia is commonly associated with malnutrition, inflammation, and severe coexisting comorbidities, which thus are suspected of being potential confounders. This study was aimed at testing whether hypokalemia was independently associated with the occurrence and prognosis of PD-associated peritonitis. Methods: A national-level dataset from the Peritoneal Dialysis Telemedicine-assisted Platform Cohort (PDTAP) Study was used to explore the independent association of serum potassium with PD-associated peritonitis. Unmatched and propensity score-adjusted multivariate competing risk models, as well as univariate competing risk models following 1:1 propensity score matching, were conducted to balance potential biases between patients with and without hypokalemia. The association between potassium levels prior to peritonitis and treatment failure due to peritonitis was also investigated. Results: During a median follow-up of 25.7 months in 7220 PD patients, there was a higher incidence of peritonitis in patients with serum potassium below 4.0 mmol/L compared to those with higher serum levels (677 [0.114/patient-year] vs. 914 [0.096/patient-year], P= 0.001). After adjusting for demographics, laboratory tests, residual renal function, and medication use, baseline potassium levels below 4.0 mmol/L were not linked to an increased risk of peritonitis, with a hazard ratio of 0.983 (95% CI 0.855–1.130, P= 0.810). This result remained consistent in both the propensity score adjusted multivariate competing risk regression (HR = 0.974, 95% CI 0.829–1.145, P= 0.750) and the univariate competing risk regression after 1:1 propensity score matching (Fine-Gray test, P= 0.218). The results were similar when analyzing patients with serum potassium level above or below 3.5 mmol/L. Lastly, hypokalemia before the occurrence of peritonitis was not independently associated with treatment failure. Conclusion: Hypokalemia was not found to be an independent risk factor for PD-associated peritonitis or treatment failure of peritonitis in China. Graphical abstract:

Background: Arteriovenous fistula (AVF) failure can occur in patients undergoing hemodialysis (HD). In this study, we explored the correlation between hyperlipidemia and AVF failure in patients undergoing HD. Moreover, we compared the lipid profiles of patients with chronic kidney disease (CKD) with those of healthy people to provide a basis for lipid-lowering in patients undergoing HD.Method and analysis: We searched PubMed, Web of Science, Embase, the Cochrane library, CNKI, CBM, the China Science Periodical Database, and the China Science and Technology Journal Database. The final search was conducted on August 31, 2021, and the search period was restricted between 2000 and August 31, 2021, without publication restrictions. All studies met the inclusion criteria, and the influences of sex, age, geographical location, diagnosis method, and publication year were excluded. The data were analyzed using the random-effects model and the fixed-effects model.Results: Twenty-eight studies were included in the meta-analysis with 121,666 patients in the CKD group and 1714 patients in the AVF failure group. Triglyceride concentration in patients with CKD was higher than in healthy subjects (MD: −31.56, 95% CI: −41.23 to −21.90, p< 0.00001). A high total cholesterol (TC) concentration (MD: 6.97, 95% CI: 2.19–11.74, p= 0.004) and a high low-density lipoprotein cholesterol (LDL-C) concentration (MD: 23.83, 95% CI: 18.48–29.18, p< 0.00001) were associated with AVF failure. Furthermore, HDL-C was lower in the AVF failure group than in the AVF patency group (MD: −2.68, 95% CI: −4.60 to −0.76, p= 0.006).Conclusion: Our analysis indicates that the AVF failure may be related to the increase of TC/LDL-C and the decrease of HDL-C. Although current guidelines do not consider intensive lipid-lowering therapy as necessary in patients undergoing HD, our research indicates that patients with AVF undergoing HD may need regular TC/LDL-C-lowering therapy to prevent AVF failure. However, this issue still needs well designed prospective trials.

The application of innovative adsorbents with high adsorption capacity and easy recovery in wastewater treatment has always been of great concern. This study employed κ-carrageenan to enhance the dispersion of magnetic nanoparticles and improve their stability through Ca2+and Na+crosslinkinelg. XRD, FTIR, SEM and EDS revealed that the magnetic nanoparticles were coated with κ-carrageenan. The synthesized MΚC@Fe3O4MNPs exhibited a stable structure and multiple adsorption sites, which was beneficial for the treatment of cationic dyes in wastewater. The absorption results indicated that abundant active sites on the surface of the adsorbent efficiently adsorbed the three cationic dyes. Through optimization experiments, the MΚC@Fe3O4MNPs demonstrated maximum adsorption capacities of 729.9 mg/g, 184.1 mg/g, and 311.0 mg/g for methylene blue, malachite green, and safranin T, respectively. The adsorbent could maintain excellent adsorption efficiency even after 5 cycles of use. The adsorption mechanisms for the three cationic dyes included electrostatic interactions, n-π interactions, and hydrogen bonding. Therefore, the MΚC@Fe3O4MNPs showed highly efficient adsorption and separation of cationic dyes and are suitable candidates for dye wastewater treatment.

Starter cultures play a significant role in lipid hydrolysis, prevention of lipid oxidation, and synthesis of fatty acid in fermented sausage, enhancing product quality. In this study, five synergistic bacterial strains were used, including Pediococcus pentosaceus(B-3), Latilactobacillus sakeiDLS-24 (D-24), Latilactobacillus acidophilusDLS-29 (D-29), Lactiplantibacillus pentosus(B-1), and Lactiplantibacillus plantarum(B-2). Sausage B1B3D24 gave the highest free fatty acid with 39.45 g/100 g at 45-Day. Based on 2-thiobarbituric acid reactive substance, B2B3 contains 112.68 MDA/kg. Lipoxygenase activity displays the lowest in B1B3D24 with 0.095 μmol/min·mg followed by B2B3 with 0.145 μmol/min·mg. B1B3D24 contains 11.35 g/kg of monounsaturated fatty acid with the highest content in eicosenoic acid (C20:1) and palmitoleic acid (C16:1). The fatty acid synthesis pathway in B1B3D24 contains an active positive interaction with PUFA to increase the isotopomers of ω-3 and ω-6 fatty acids. In addition, lipid mediating genes in B1B3D24 show the highest counts in fatty-acid synthase, carbonyl reductase 4, 3-oxoacyl-[acyl-carrier-protein] synthase III, hydroxysteroid 17-beta dehydrogenase 8, and acetyl-CoA carboxylase.

Peptide-based therapeutics hold immense promise for the treatment of various diseases. However, their effectiveness is often hampered by poor cell membrane permeability, hindering targeted intracellular delivery and oral drug development. This study addressed this challenge by introducing a novel graph neural network (GNN) framework and advanced machine learning algorithms to build predictive models for peptide permeability. Our models offer systematic evaluation across diverse peptides (natural, modified, linear and cyclic) and cell lines [Caco-2, Ralph Russ canine kidney (RRCK) and parallel artificial membrane permeability assay (PAMPA)]. The predictive models for linear and cyclic peptides in Caco-2 and RRCK cell lines were constructed for the first time, with an impressive coefficient of determination (R2) of 0.708, 0.484, 0.553, and 0.528 in the test set, respectively. Notably, the GNN framework behaved better in permeability prediction with larger data sets and improved the accuracy of cyclic peptide prediction in the PAMPA cell line. The R2increased by about 0.32 compared with the reported models. Furthermore, the important molecular structural features that contribute to good permeability were interpreted; the influence of cell lines, peptide modification, and cyclization on permeability were successfully revealed. To facilitate broader use, we deployed these models on the user-friendly KNIME platform (https://github.com/ifyoungnet/PharmPapp). This work provides a rapid and reliable strategy for systematically assessing peptide permeability, aiding researchers in drug delivery optimization, peptide preselection during drug discovery, and potentially the design of targeted peptide-based materials.

Background: Although myotomy is crucial in peroral endoscopic myotomy (POEM) surgeries, its optimum length remains controversial. Herein, we propose a modified POEM with new method of tailoring myotomy length aim to evaluate the safety, efficacy, and clinical outcomes of this modified POEM compared with standard POEM in type I or II achalasia. Methods: Seventy-five patients with type I or II achalasia who underwent POEM at the First Hospital of Jilin University between January 2018 and December 2022 were retrospectively analyzed. According to the myotomy approach, these patients were divided into the retrograde on-demand myotomy (RDM, n= 34), with myotomy beginning on gastric side and length tailored by determining the degree of lower esophageal sphincter (LES) distention, and standard myotomy (SM, n= 41) groups. The baseline data, myotomy length, operation time, clinical success rate, adverse event rate, and reflux-related adverse events were compared and analyzed. Results: The median myotomy length in the RDM group was significantly shorter than that in the SM group (6 vs. 8 cm, respectively; p&lt; 0.001). Moreover, the median myotomy time in the RDM group was significantly shorter than that in the SM group (10 vs. 16 min, respectively; p&lt; 0.001). POEM was successfully performed in all the patients. At the 2-year follow-up, high clinical success rates were observed in both the RDM and SM groups (92.0% vs. 93.3%, respectively; p= 1.000). The incidence of intraoperative adverse events and postoperative reflux-related adverse events was low and comparable in both groups. Conclusions: RDM POEM is a safe and effective method for patients with type I or II achalasia. Furthermore, it has a shorter myotomy length and operation time than standard POEM technique.

Point clouds have garnered increasing research attention and found numerous practical applications. However, many of these applications, such as autonomous driving and robotic manipulation, rely on sequential point clouds, essentially adding a temporal dimension to the data (i.e., four dimensions) because the information of the static point cloud data could provide is still limited. Recent research efforts have been directed towards enhancing the understanding and utilization of sequential point clouds. This paper offers a comprehensive review of deep learning methods applied to sequential point cloud research, encompassing dynamic flow estimation, object detection & tracking, point cloud segmentation, and point cloud forecasting. This paper further summarizes and compares the quantitative results of the reviewed methods over the public benchmark datasets. Ultimately, the paper concludes by addressing the challenges in current sequential point cloud research and pointing towards promising avenues for future research.

DPOB molecules passivated interface defects, released residual strain, and induced band bending to enhance hole extraction and transport. The champion PCE of CsPbBr3PSC achieved 10.87%.

The etiology of Hashimoto's thyroiditis (HT) involves genetic and environmental factors. There is a lack of clarity regarding the relationship between Vitamin D and HT. This study aimed to investigate the effect of Vitamin D and gene polymorphisms on thyroid peroxidase antibody (TPOAb) positivity.

Uncertainty exists about whether lowering systolic blood pressure to less than 120 mm Hg is superior to that of less than 140 mm Hg, particularly in patients with diabetes and patients with previous stroke.

Disruption of the symbiosis of extra/intratumoral metabolism is a good strategy for treating tumors that shuttle resources from the tumor microenvironment. Here, we report a precision treatment strategy for enhancing pyruvic acid and intratumoral acidosis to destroy tumoral metabolic symbiosis to eliminate tumors; this approach is based on PEGylated gold and lactate oxidase-modified aminated dendritic mesoporous silica with lonidamine and ferrous sulfide loading (PEG-Au@DMSNs/FeS/LND@LOX). In the tumor microenvironment, LOX oxidizes lactic acid to produce pyruvate, which represses tumor cell proliferation by inhibiting histone gene expression and induces ferroptosis by partial histone monoubiquitination. In acidic tumor conditions, the nanoparticles release H2S gas and Fe2+ions, which can inhibit catalase activity to promote the Fenton reaction of Fe2+, resulting in massive ·OH production and ferroptosis via Fe3+. More interestingly, the combination of H2S and LND (a monocarboxylic acid transporter inhibitor) can cause intracellular acidosis by lactate, and protons overaccumulate in cells. Multiple intracellular acidosis is caused by lactate-pyruvate axis disorders. Moreover, H2S provides motive power to intensify the shuttling of nanoparticles in the tumor region. The findings confirm that this nanomedicine system can enable precise antitumor effects by disrupting extra/intratumoral metabolic symbiosis and inducing ferroptosis and represents a promising active drug delivery system candidate for tumor treatment.

Sm-Fe-N material holds immense promise as new material for developing next generation permanent magnets. However, the achievement of high coercivity in this material remains a big challenge, and moreover new coercivity theories beyond the commonly considered nucleation-type mechanism needs exploration. This study describes both the yielding of anisotropic Sm-Fe-N powders with high coercivities and the discovery of new coercivity mechanism by a systematic study using a designed milling preparation method. As a result, a random-direction cleavage behavior with unexpected XRD peak shifts and formation of new micron-sized flower-like particles were found in the yielded Sm-Fe-N powders. The grain sizes of the powders are of nanoscale dimension and they refine gradually as the milling time increases. The measured coercivity is up to 15.0 kOe, which is a substantial increase of approximately 36.4% compared to the original commercial powders and among the highest reported values via the milling preparation method (commonly below 13 kOe). The yielded powders also demonstrate a new dual-type coercivity mechanism (combing both nucleation-type and pinning-type control effect), and the improved pining effect contributes to the high coercivities. This study opens the door of utilizing pinning effect for achieving high coercivities in anisotropic Sm-Fe-N material.

Two-dimensional (2D) materials with superior properties exhibit tremendous potential in developing next-generation electronic and optoelectronic devices. Integrating various functions into one device is highly expected as that endows 2D materials great promise for more Moore and more-than-Moore device applications. Here, we construct a WSe2/Ta2NiSe5heterostructure by stacking the p-type WSe2and the n-type narrow gap Ta2NiSe5with the aim to achieve a multifunction optoelectronic device. Owing to the large interface potential barrier, the heterostructure device reveals a prominent diode feature with a large rectify ratio (7.6 × 104) and a low dark current (10–12A). Especially, gate voltage- and bias voltage-tunable staggered-gap to broken-gap transition is achieved on the heterostructure device, which enables gate voltage-tunable forward and reverse rectifying features. As results, the heterostructure device exhibits superior self-powered photodetection properties, including a high detectivity of 1.08 × 1010Jones and a fast response time of 91 µs. Additionally, the intrinsic structural anisotropy of Ta2NiSe5endows the heterostructure device with strong polarization-sensitive photodetection and high-resolution polarization imaging. Based on these characteristics, a multimode optoelectronic logic gate is realized on the heterostructure via synergistically modulating the light on/off, polarization angle, gate voltage, and bias voltage. This work shed light on the future development of constructing high-performance multifunctional optoelectronic devices.

Inconsistent results have been reported regarding the association between low-to-moderate arsenic (As) exposure and diabetes. The effect of liver dysfunction on As-induced diabetes remains unclear. The cross-sectional study included 10,574 adults from 2017–2018 China National Human Biomonitoring. Urinary total As (TAs) levels were analyzed as markers of As exposure. Generalized linear mixed models and restricted cubic splines models were used to examine the relationships among TAs levels, serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) concentrations, and diabetes prevalence. Mediating analysis was performed to assess whether liver dysfunction mediated the association between TAs and diabetes. Overall, the OR (95% CI) of diabetes in participants in the second, third, and fourth quartiles of TAs were 1.08 (0.88, 1.33), 1.17 (0.94, 1.45), and 1.52 (1.22, 1.90), respectively, in the fully adjusted models compared with those in the lowest quartile. Serum ALT was positively associated with TAs and diabetes. Additionally, mediation analyses showed that ALT mediated 4.32% of the association between TAs and diabetes in the overall population and 8.86% in the population without alcohol consumption in the past year. This study suggested that alleviating the hepatotoxicity of As could have implications for both diabetes and liver disease.

Robots’ manipulation accuracy is heavily determined by the calibration accuracy of the tool center point (TCP). This article proposes posture-sequence particle swarm optimization (PS2O) for TCP calibration. First, the mechanism of the condition number and the minimum eigenvalue of the regression matrix on the calibration accuracy are analyzed. Second, a multiobjective optimization problem is constructed based on the condition number, the minimum eigenvalue, and the sum of adjacent posture distances. Finally, an optimized posture sequence is obtained by applying the particle swarm optimization (PSO) algorithm to the constructed multiobjective optimization problem. Simulations and experiments demonstrate that the error of TCP calibration can be characterized by the condition number and the minimum eigenvalue of the regression matrix. Compared with random posture sampling, the optimized posture sequence for sampling reduces the norm of the calibration error by 38.66%. Thus, PS2O has great potential to improve robots’ manipulation accuracy.

Background: Ulcerative colitis (UC) is a debilitating chronic inflammatory bowel disease, and current treatments primarily focus on suppressing inflammation with limited efficacy. However, the resolution of inflammation also plays a crucial role in UC prognosis. Combining anti-inflammatory and pro-inflammatory resolution interventions may be a promising approach for treating UC. Materials and methods: The nano-bomb nanoparticles were validated for their ability to load CD98 siRNA (siCD98) and Annexin A1-mimetic peptides (Ac2-26 peptides), as well as release CO2upon lysosomal escape. Surface modification with hyaluronic acid (HA) was assessed for its capability to target inflammatory tissues and cells. Biocompatibility and biosafety were evaluated through in vitroand in vivostudies. The anti-inflammatory and pro-resolving effects of siCD98@NPs and Ac2-26@NPs, both individually and in combination, were evaluated by measuring ROS production, pro-inflammatory cytokine expression, CD98 gene expression, and macrophage polarization. Results: These nanoparticles could efficiently load siCD98 and Ac2-26 peptides and release CO2under acidic pH in the endo/lysosome to deliver drugs to the cytoplasm. HA could effectively target the inflammatory tissue and cells, showing good biocompatibility and biosafety both in vitroand in vivo. siCD98@NPs and Ac2-26@NPs showed anti-inflammatory effects by eliminating the over-production of ROS and down-regulating the expression of pro-inflammatory cytokines (TNF-α and IL-1β) and the CD98 gene; meanwhile, it showed pro-resolving function by inhibiting M0 to pro-inflammatory M1 macrophage conversion, with a more pronounced effect when combined with siCD98 and Ac2-26. The oral administration of chitosan-alginate hydrogel-encapsulated nanoparticles in UC model mice effectively alleviated inflammatory symptoms, reduced the expression of pro-inflammatory cytokines (TNF-α and IL-1β) and the CD98 gene, restored intestinal barrier function, and promoted M1 to M2 polarization, with a more pronounced effect when combined. Conclusion: By combining anti-inflammatory and pro-resolution interventions, these nanoparticles offer a novel therapeutic approach. This study offered a new approach for combination therapy of UC.

Study design: Experimental animal study. Objectives: To investigate the protective effect of remote limb ischemia preconditioning (RLPreC) on traumatic spinal cord injury (SCI) and explore the underlying biological mechanisms using RNA sequencing. Setting: China Rehabilitation Science Institute; Beijing; China. Methods: spinal cord injury was induced in mice using a force of 0.7 N. RLPreC treatment was administered. Motor function, pain behavior, and gene expression were assessed. Results: RLPreC treatment significantly improved motor function and reduced pain-like behavior in SCI mice. RNA-Seq analysis identified 5247 differentially expressed genes (DEGs). GO analysis revealed enrichment of immune response, inflammatory signaling, and synaptic transmission pathways among these DEGs. KEGG analysis indicated suppression of inflammation and promotion of synapse-related pathways. Conclusions: RLPreC is a promising therapeutic strategy for improving motor function and alleviating pain after traumatic SCI. RNA-Seq analysis provides insights into potential therapeutic targets and warrants further investigation.

Noninvasively and accurately predicting the epidermal growth factor receptor (EGFR) mutation status is a clinically vital problem. Moreover, further identifying the most suspicious area related to the EGFR mutation status can guide the biopsy to avoid false negatives. Deep learning methods based on computed tomography (CT) images may improve the noninvasive prediction of EGFR mutation status and potentially help clinicians guide biopsies by visual methods. Inspired by the potential inherent links between EGFR mutation status and invasiveness information, we hypothesized that the predictive performance of a deep learning network can be improved through extra utilization of the invasiveness information. Here, we created a novel explainable transformer network for EGFR classification named gated multiple instance learning transformer (GMILT) by integrating multi-instance learning and discriminative weakly supervised feature learning. Pathological invasiveness information was first introduced into the multitask model as embeddings. GMILT was trained and validated on a total of 512 patients with adenocarcinoma and tested on three datasets (the internal test dataset, the external test dataset, and The Cancer Imaging Archive (TCIA) public dataset). The performance (area under the curve (AUC) $=0.772$ on the internal test dataset) of GMILT exceeded that of previously published methods and radiomics-based methods (i.e., random forest and support vector machine) and attained a preferable generalization ability (AUC $=0.856$ in the TCIA test dataset and AUC $=0.756$ in the external dataset). A diameter-based subgroup analysis further verified the efficiency of our model (most of the AUCs exceeded 0.772) to noninvasively predict EGFR mutation status from computed tomography (CT) images. In addition, because our method also identified the “core area” of the most suspicious area related to the EGFR mutation status, it has the potential ability to guide biopsies.

RNA-binding proteins (RBPs) are pivotal in acute myeloid leukaemia (AML), a lethal disease. Although specific phase separation-competent RBPs are recognized in AML, the effect of their condensate formation on AML leukaemogenesis, and the therapeutic potential of inhibition of phase separation are underexplored. In our in vivo CRISPR RBP screen, fibrillarin (FBL) emerges as a crucial nucleolar protein that regulates AML cell survival, primarily through its phase separation domains rather than methyltransferase or acetylation domains. These phase separation domains, with specific features, coordinately drive nucleoli formation and early processing of pre-rRNA (including efflux, cleavage and methylation), eventually enhancing the translation of oncogenes such as MYC. Targeting the phase separation capability of FBL with CGX-635 leads to elimination of AML cells, suggesting an additional mechanism of action for CGX-635 that complements its established therapeutic effects. We highlight the potential of PS modulation of critical proteins as a possible therapeutic strategy for AML.

A biosensor based on self-assembled ssDNA(aptamer) and polyethylene glycol functionalized graphene oxide(GO-PEG) has been designed for sensing profenofos in food. The sensor has employed the fluorescence “on/off” switching strategy in a single step in homogeneous solution. Compared to traditional detection methods, the strategy proposed here is simple, convenient, fast and sensitive. Furthermore, compared with the general aptamer-GO structure, this aptamer-GO-PEG structure is in possession of a better detection performance, which is largely attributed to the improvement of the biocompatibility and the adjustment of the adsorption capacity of GO by grafting the blocking agent PEG onto the surface of GO. Additionally, the improved biocompatibility of GO shows better stability in salt solutions and physiological solutions, which is more conducive to its practical application in foods. In this project, profenofos had been detected with the proposed strategy, and the limit of detection has been controlled to be 0.21 ng/mL. This aptasensing assay has been applied to determining profenofos in (spiked)tap water, cabbage and milk with the recovery values ranging from 93.1% to 108.5%, from 90.8% to 113.2% and from 105.9% to 114.2%, respectively.

Background: Zhihu live talks, as a major online knowledge commodity, enable speakers to provide professional information and interact with the audiences. The use of hedges and boosters has been associated with the realization of such a goal. Literature review: Previous research has indicated the relevance of disciplines or genres in the use of hedges and boosters in academic discourse; however, little is known about the use of these metadiscourse markers in Zhihu live talks as a new register for popularizing professional knowledge. Research questions: 1. What are the disciplinary variations in the use of hedges and boosters in medical science and health (Med) and education (Edu) live talks? 2. To what extent do speakers’ characteristics (i.e., expertise and community status) have an impact on the use of hedges and boosters in Med and Edu live talks? Research methods: We collected the transcripts of 123 Med and 126 Edu live talks, as well as the demographic information of each speaker. Following a framework adapted from Hu and Cao, we conducted an analysis of the frequencies and functions of hedges and boosters, and how they associate with speaker characteristics in each category of live talks. Results: The two corpora exhibited significant differences in the frequencies and functions of hedges/boosters, and the differences can be attributed to the conventions of knowledge making in medicine and education disciplines. In addition, speaker characteristics have some impact on the use of hedges and boosters, such as speakers’ levels of conformity to disciplinary conventions or their strategic efforts in relational management. Conclusion: The findings can guide different speakers to configure metadiscourse to inform, argue, and direct while popularizing professional knowledge of different disciplines.

A detailed analysis of the electrical response of In0.3Ga0.7As surface quantum dots (SQDs) coupled to 5-layer buried quantum dots (BQDs) is carried out as a function of ethanol and acetone concentration while temperature-dependent photoluminescence (PL) spectra are also analyzed. The coupling structure is grown by solid source molecular beam epitaxy. Carrier transport from BQDs to SQDs is confirmed by the temperature-dependent PL spectra. The importance of the surface states for the sensing application is once more highlighted. The results show that not only the exposure to the target gas but also the illumination affect the electrical response of the coupling sample strongly. In the ethanol atmosphere and under the illumination, the sheet resistance of the coupling structure decays by 50% while it remains nearly constant for the reference structure with only the 5-layer BQDs but not the SQDs. The strong dependence of the electrical response on the gas concentration makes SQDs very suitable for the development of integrated micrometer-sized gas sensor devices.

With the remarkable success of digital histopathology and the deep learning technology, many whole-slide pathological images (WSIs) based deep learning models are designed to help pathologists diagnose human cancers. Recently, rather than predicting categorical variables as in cancer diagnosis, several deep learning studies are also proposed to estimate the continuous variables such as the patients’ survival or their transcriptional profile. However, most of the existing studies focus on conducting these predicting tasks separately, which overlooks the useful intrinsic correlation among them that can boost the prediction performance of each individual task. In addition, it is sill challenge to design the WSI-based deep learning models, since a WSI is with huge size but annotated with coarse label. In this study, we propose a general multi-instance multi-task learning framework (HistMIMT) for multi-purpose prediction from WSIs. Specifically, we firstly propose a novel multi-instance learning module (TMICS) considering both common and specific task information across different tasks to generate bag representation for each individual task. Then, a soft-mask based fusion module with channel attention (SFCA) is developed to leverage useful information from the related tasks to help improve the prediction performance on target task. We evaluate our method on three cancer cohorts derived from the Cancer Genome Atlas (TCGA). For each cohort, our multi-purpose prediction tasks range from cancer diagnosis, survival prediction and estimating the transcriptional profile of gene TP53. The experimental results demonstrated that HistMIMT can yield better outcome on all clinical prediction tasks than its competitors.

Background: Non-invasive renal fibrosis assessment is critical for tailoring personalized decision-making and managing follow-up in patients with chronic kidney disease (CKD). We aimed to exploit machine learning algorithms using clinical and elastosonographic features to distinguish moderate-severe fibrosis from mild fibrosis among CKD patients. Methods: A total of 162 patients with CKD who underwent shear wave elastography examinations and renal biopsies at our institution were prospectively enrolled. Four classifiers using machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and K-Nearest Neighbor (KNN), which integrated elastosonographic features and clinical characteristics, were established to differentiate moderate-severe renal fibrosis from mild forms. The area under the receiver operating characteristic curve (AUC) and average precision were employed to compare the performance of constructed models, and the SHapley Additive exPlanations (SHAP) strategy was used to visualize and interpret the model output. Results: The XGBoost model outperformed the other developed machine learning models, demonstrating optimal diagnostic performance in both the primary (AUC = 0.97, 95% confidence level (CI) 0.94–0.99; average precision = 0.97, 95% CI 0.97–0.98) and five-fold cross-validation (AUC = 0.85, 95% CI 0.73–0.98; average precision = 0.90, 95% CI 0.86–0.93) datasets. The SHAP approach provided visual interpretation for XGBoost, highlighting the features’ impact on the diagnostic process, wherein the estimated glomerular filtration rate provided the largest contribution to the model output, followed by the elastic modulus, then renal length, renal resistive index, and hypertension. Conclusion: This study proposed an XGBoost model for distinguishing moderate-severe renal fibrosis from mild forms in CKD patients, which could be used to assist clinicians in decision-making and follow-up strategies. Moreover, the SHAP algorithm makes it feasible to visualize and interpret the feature processing and diagnostic processes of the model output. Graphical Abstract:

Silicon piezoresistive pressure sensors suffer from temperature effects significantly. The thermal compensation system based on surface fitting and wild horse algorithm is proposed to overcome the nonlinearity. The polynomial coefficients in surface fitting are optimized using the wild horse algorithm. A platinum resistance thermometer (PRT) integrated in the silicon layer of the pressure sensor is employed to capture the local temperature variations. The extracted pressure and temperature changes are fed to the compensation algorithm via digital circuits. The results of calibration experiments show that the PRT has a maximum absolute error of $1.24 \Omega $ , and the standard deviation of the output signal and sensitivity among several pressure sensors in the same batch is 4.5 mV and 0.07 mV/MPa, respectively, ensuring the accuracy for PRT and mass production repeatability for pressure sensors. The comprehensive performance of the compensation system was validated under 0–40 MPa and 0 °C–80 °C. The linearity of the sensor decreases from 8.1218%FS to 0.0629%FS on the calibration data. When the pressure and temperature change simultaneously, the linearity remains at 0.1112%FS. Hence, the system considerably reduces the temperature impact on silicon piezoresistive pressure sensors and exhibits strong integration and generalization.