Browse Articles
Discover research articles across all indexed journals
Research on recycling value grading and real-time perception of rock debris from TBM tunneling
Abstract During the construction of TBM tunnels, a substantial quantity of rock debris is generated, leading to significant land occupation and environmental pollution. Recycling rock debris into construction materials and other resources emerges as a viable solution to these problems. To realize the continuous classified storage and disposal of tunnel rock debris, this research explores the four-level processing network, establishes an objective function for evaluating the recycling value of tunnel rock debris during TBM tunneling, and grades the recycling value by calculating the weight and similarity of their performance indicators (uniaxial compressive strength, content of acicular and flattened particles, mud content, and crushing index) through the TOPSIS method. Through correlation and weight analysis, we identify five key characteristics, i.e. cutterhead torque, tool penetration, cutterhead thrust, advancing rate, and support shoe pump pressure, to conduct real-time perception of the recycling value level of rock debris. Leveraging a comprehensive database that encompasses both tunnel rock debris performance indicators and TBM tunneling parameters, perception models are constructed using different machine learning algorithms. After Bayesian hyperparameter optimization, the perception models based on CART, SVM, KNN, and ANN demonstrate accuracies of 67.5%, 80.0%, 82.5%, and 83.8% respectively. Notably, the hyperparameter optimization significantly enhances the accuracy of the ANN perception model. When applying the optimized ANN-based rock debris recycling value grade perception model to TBM tunnel engineering, the tested perception accuracy rate stands at 83.3%, demonstrating its effectiveness and potential for practical applications. This approach provides valuable guidance for the graded storage and efficient recycling of tunnel rock debris and helps to alleviate the pollution problem.
Superpowers want to control critical mineral supplies — local communities need a stronger say
Cooperation in the face of disaster
As calamities and health crises are expected to recur and become more frequent, we rely more on cooperation to prevent similar situations and to cope with their aftermaths. However, it is not clear if, how and why people cooperate in uncertain situations where losses can result from inadequate cooperation. Through theoretical modelling, experiments and simulations, we show the behavioural patterns driving cooperation in a stochastic environment. Specifically, by introducing stochastic shocks to a threshold public goods game where one can randomly incur losses when group contributions are below a specific level, we investigate what happens to cooperation when disasters strike repeatedly. The findings show that compared to a control setting, cooperation is higher and persists when there is a risk for disasters to strike, and that this is sustained by unconditional cooperation. People give more and do not match the contributions of others, contrasting the conditionality observed in deterministic environments. In other words, we observe a contribution divergence in uncertain environments wherein some give unconditionally while others free-ride. We study three different types of uncertainty about the disaster: the probability of a disaster, additionally if it is uncertain how much cooperation is required to avoid them (threshold level), and how much losses will be incurred (impact). The results are similar in countries having different natural disaster risks, the Philippines and Sweden. Simulating for a longer time period suggests the importance of promoting unconditionality to foster sustained cooperation in facing an uncertain world.
Polygenic score analysis identifies distinct genetic risk profiles in Alzheimer’s disease comorbidities
Comprehensive analysis of CMTM family and immune infiltration in esophageal carcinoma
Objective Esophageal carcinoma (ESCA) is one of the most common malignant diseases and contributes to the annual burden of death worldwide. A better understanding of the underlying molecular changes is urgently required to identify early diagnostic biomarkers and effective therapeutics. The chemokine-like factor (CKLF)-like MARVEL transmembrane domain-containing family (CMTMs) is reported to be entangled in many human cancers. However, the role of CMTMs in ESCA remains unclear. Methods The differential expressions of CMTMs between ESCA and normal tissues were analyzed using TCGA database. The relationships between CMTMs and immune infiltration in the tumor microenvironment (TME) were also evaluated to explore their underlying values in the diagnosis and prognosis of ESCA. Results The results showed that ESCA showed significantly higher expressions of CMTM1,3,6,7 and lower expressions of CMTM4,5 than normal tissue (P < 0.05). Meanwhile, CMTM3,4,8 expressions were correlated with the tumor stage of ECSA patients. The analysis on immune infiltrations (CD8 + T, Tregs, NK and macrophages) showed that M2 macrophages was dominant in TME, with significantly higher levels than the other cells (F = 326.93, P < 0.001). The higher abundance of M2 macrophages and Tregs significantly shortened the survival time of patients with ESCA (P = 0.01). Interestingly, the expression levels of CMTM1,3,5,7 were comparable to the abundance of M2 macrophages (CMTM1: r = 0.172168; CMTM3: r = 0.313221; CMTM5: r = 0.130669; CMTM7: r = 0.119922; P < 0.05). CMTM2,4,5,7,8 positively correlated with Tregs (P < 0.05). Moreover, we found positive associations between the expression of CMTMs and the signatures of M2 macrophages (MS4A4A, VSIG4 and CD163). Conclusion There were differential expressions of CMTMs between ESCA and normal tissues. Furthermore, the expression of CMTMs was positively correlated with M2 macrophages, indicating a possibility that CMTMs may become a new immunotherapy target for ESCA.
Evaluation on the interface characteristics, mechanism and performance of the dry modified SBS asphalt mixtures by multiscale methods
Using artificial intelligence tools to automate data extraction for living evidence syntheses
Living evidence synthesis (LES) involves repeatedly updating a systematic review or meta-analysis at regular intervals to incorporate new evidence into the summary results. It requires a considerable amount of human time investment in the article search, collection, and data extraction phases. Tools exist to automate the retrieval of relevant journal articles, but pulling data out of those articles is currently still a manual process. In this article, we present a proof-of-concept Python program that leverages artificial intelligence (AI) tools (specifically, ChatGPT) to parse a batch of journal articles and extract relevant results, greatly reducing the human time investment in this action without compromising on accuracy. Our program is tested on a set of journal articles that estimate the mean incubation period for COVID-19, an epidemiological parameter of importance for mathematical modelling. We also discuss important limitations related to the total amount of information and rate at which that information can be sent to the AI engine. This work contributes to the ongoing discussion about the use of AI and the role such tools can have in scientific research.
Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3–5 and end-stage kidney disease
Minerals will shape future geopolitical order
Entropy difference-based EEG channel selection technique for automated detection of ADHD
Attention deficit hyperactivity disorder (ADHD) is one of the common neurodevelopmental disorders in children. This paper presents an automated approach for ADHD detection using the proposed entropy difference (EnD)-based encephalogram (EEG) channel selection approach. In the proposed approach, we selected the most significant EEG channels for the accurate identification of ADHD using an EnD-based channel selection approach. Secondly, a set of features is extracted from the selected channels and fed to a classifier. To verify the effectiveness of the channels selected, we explored three sets of features and classifiers. More specifically, we explored discrete wavelet transform (DWT), empirical mode decomposition (EMD) and symmetrically-weighted local binary pattern (SLBP)-based features. To perform automated classification, we have used k-nearest neighbor (k-NN), Ensemble classifier, and support vectors machine (SVM) classifiers. Our proposed approach yielded the highest accuracy of 99.29% using the public database. In addition, the proposed EnD-based channel selection has consistently provided better classification accuracies than the entropy-based channel selection approach. Also, the developed method has outperformed the existing approaches in automated ADHD detection.
Glucagon like peptide-1 modulates urinary sodium excretion in diabetic kidney disease via ENaC activation
Development of a multivariable prognostic prediction model for skin tears in older nursing home residents
Abstract Skin tears are traumatic wounds and are among the most prevalent skin conditions in older adults, particularly those in long-term care facilities. These injuries can lead to complications such as infection, pain, reduced quality of life, and increased healthcare costs. This study aimed to identify risk factors for skin tear development in nursing home residents aged 65 years or older and to develop a predictive prognostic model. A secondary data analysis was performed on long-term care nursing home residents ≥ 65 years who participated in a cluster-randomized controlled clinical trial conducted in Berlin, Germany, from April 2019 to June 2021. A total of 101 residents were included. At week 12, 19 residents (18.8%) developed at least one skin tear. The best-fit predictive model identified lower Body Mass Index, lower Barthel Index scores, presence of xerosis cutis on the legs, and regular corticosteroid use as significant risk factors for skin tear development. The model demonstrated good discriminatory ability (area under the curve: 0.823), with sensitivity and specificity rates of 73.7% and 74.4%, respectively. These risk factors could help identify at-risk individuals, enabling targeted preventive measures. However, the model requires validation in a prospective cohort to confirm its applicability in clinical practice.
Creation of knockin mice for the fluorescence protein based in vivo identification of skeletal myofiber types
A phase 1 trial of prizloncabtagene autoleucel, a CD19/CD20 CAR T-cell therapy for relapsed/refractory B-cell non-Hodgkin lymphoma
Abstract Prizloncabtagene autoleucel (prizlon-cel), a novel bispecific chimeric antigen receptor T cell, targets and eliminates CD19/CD20-positive tumor cells. This phase 1, open-label study investigated the safety and efficacy of prizlon-cel in patients with relapsed/refractory B-cell non-Hodgkin lymphoma (R/R B-NHL). Patients with CD19 and/or CD20-positive R/R B-NHL received a 3-day lymphodepletion (cyclophosphamide: 300 mg/m2 per day; fludarabine: 30 mg/m2 per day) followed by an IV dose of prizlon-cel. The primary end points were dose-limiting toxicity (DLT) and incidence and severity of treatment-emergent adverse events (TEAEs). Secondary end points included overall response rate (ORR), duration of response (DOR), progression-free survival (PFS), and overall survival (OS). Of the 48 patients infused prizlon-cel, 44 had large B-cell lymphoma (LBCL). No patient experienced DLT. Cytokine release syndrome occurred in 93.8% of the patients, with only 1 case of grade 3. Immune effector cell-associated neurotoxicity syndrome occurred in 6.3% of patients, with no grade 3 or higher events. The most common grade 3 or higher TEAEs were neutropenia (83.3%) and leukopenia (50%). The ORR and complete response (CR) rates in all patients were 91.5% and 85.1%, respectively, and in LBCL patients, ORR was 90.7% with 86.0% CR. With median follow-up of 30.0 months, median DOR, PFS, and OS were all not reached. Kaplan-Meier estimate of 2-year DOR, PFS, and OS rates were 66.0%, 62.6%, and 76.5%, respectively. Prizlon-cel had a favorable safety profile and a high and durable response in patients with R/R B-NHL, suggesting a promising treatment option for patients with R/R B-NHL. These trials were registered at www.clinicaltrials.gov as #NCT04317885, #NCT04655677, #NCT04696432, and #NCT04693676.
Dementia classification using two-channel electroencephalography features
A brain drain would impoverish the United States and diminish world science
X-linked sideroblastic anemia in females
Abstract X-linked sideroblastic anemia (XLSA) in female carriers of 5-aminolevulinic acid synthase 2 mutations is not uncommon. We describe unique features and genotype/phenotype correlations in females with XLSA and evaluate the contributions of X-chromosome skewing and clonal hematopoiesis, emphasizing the importance of distinguishing it from myelodysplastic syndromes with ring sideroblasts.
Identifying six single nucleotide variants in the COL17A1 gene that alter RNA splicing: database analysis and minigene assays
Incorporation of immunotherapy into frontline treatment for adults with B-cell precursor acute lymphoblastic leukemia
Abstract Although complete remission rates in adults with B-cell precursor acute lymphoblastic leukemia (BCP-ALL) have improved over the last 2 decades, it is still inferior to that of the pediatric population, and once in remission, the risk of relapse is still high. Furthermore, although pediatric-inspired chemotherapy regimens have improved long-term outcomes for adolescents and young adults, these intensive chemotherapy regimens are not well tolerated in older patients and are associated with higher morbidity and mortality. Immunotherapeutic agents offer a potential opportunity to improve response and decrease relapse without increasing toxicity. The incorporation of rituximab (anti-CD20 monoclonal antibody) into chemotherapy regimens has been shown to improve outcomes. The treatment of BCP-ALL in adults has been transformed with the approval of inotuzumab ozogamicin (anti-CD22 antibody-drug conjugate), blinatumomab (CD3/CD19 bispecific antibody construct), and chimeric antigen receptor T cells for relapsed or refractory disease and of blinatumomab for measurable residual disease (MRD)–positive remission. More recently, studies of inotuzumab and blinatumomab have shown promising results when used up front either with or without multiagent chemotherapy. Blinatumomab has also been shown in a randomized trial to provide a survival benefit in patients with MRD-negative first remission when added to chemotherapy, which recently led to its additional US Food and Drug Administration approval for use in consolidation. In this review, we highlight the evolution of chemoimmunotherapy-based treatment approaches in the management of treatment-naïve BCP-ALL.