Apparatus and method for intra-layer modulation of the material deposition and assist beam and the multilayer structure produced therefrom

A method of producing a multilayer structure that has reduced interfacial roughness and interlayer mixing by using a physical-vapor deposition apparatus. In general the method includes forming a bottom layer having a first material wherein a first plurality of monolayers of the first material is deposited on an underlayer using a low incident adatom energy. Next, a second plurality of monolayers of the first material is deposited on top of the first plurality of monolayers of the first material using a high incident adatom energy. Thereafter, the method further includes forming a second layer having a second material wherein a first plurality of monolayers of the second material is deposited on the second plurality of monolayers of the first material using a low incident adatom energy. Next, a second plurality of monolayers of the second material is deposited on the first plurality of monolayers of the second material using a high incident adatom energy

Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models

Graphs are pervasive in the real-world, such as social network analysis, bioinformatics, and knowledge graphs. Graph neural networks (GNNs) have great ability in node classification, a fundamental task on graphs. Unfortunately, conventional GNNs still face challenges in scenarios with few labeled nodes, despite the prevalence of few-shot node classification tasks in real-world applications. To address this challenge, various approaches have been proposed, including graph meta-learning, transfer learning, and methods based on Large Language Models (LLMs). However, traditional meta-learning and transfer learning methods often require prior knowledge from base classes or fail to exploit the potential advantages of unlabeled nodes. Meanwhile, LLM-based methods may overlook the zero-shot capabilities of LLMs and rely heavily on the quality of generated contexts. In this paper, we propose a novel approach that integrates LLMs and GNNs, leveraging the zero-shot inference and reasoning capabilities of LLMs and employing a Graph-LLM-based active learning paradigm to enhance GNNs\u27 performance. Extensive experiments demonstrate the effectiveness of our model in improving node classification accuracy with considerably limited labeled data, surpassing state-of-the-art baselines by significant margins.10 pages, 3 Figure

A customised down-sampling machine learning approach for sepsis prediction

ObjectiveSepsis is a life-threatening condition in the ICU and requires treatment in time. Despite the accuracy of existing sepsis prediction models, insufficient focus on reducing alarms could worsen alarm fatigue and desensitisation in ICUs, potentially compromising patient safety. In this retrospective study, we aim to develop an accurate, robust, and readily deployable method in ICUs, only based on the vital signs and laboratory tests.MethodsOur method consists of a customised down-sampling process and a specific dynamic sliding window and XGBoost to offer sepsis prediction. The down-sampling process was applied to the retrospective data for training the XGBoost model. During the testing stage, the dynamic sliding window and the trained XGBoost were used to predict sepsis on the retrospective datasets, PhysioNet and FHC.ResultsWith the filtered data from PhysioNet, our method achieved 80.74% accuracy (77.90% sensitivity and 84.42% specificity) and 83.95% (84.82% sensitivity and 82.00% specificity) on the test set of PhysioNet-A and PhysioNet-B, respectively. The AUC score was 0.89 for both datasets. On the FHC dataset, our method achieved 92.38% accuracy (88.37% sensitivity and 95.16% specificity) and 0.98 AUC score on the test set of FHC.ConclusionOur results indicate that the down-sampling process and the dynamic sliding window with XGBoost brought robust and accurate performance to give sepsis prediction under various hospital settings. The localisation and robustness of our method can assist in sepsis diagnosis in different ICU settings

The diploid genome sequence of an Asian individual

Here we present the first diploid genome sequence of an Asian individual. The genome was sequenced to 36-fold average coverage using massively parallel sequencing technology. We aligned the short reads onto the NCBI human reference genome to 99.97% coverage, and guided by the reference genome, we used uniquely mapped reads to assemble a high-quality consensus sequence for 92% of the Asian individual's genome. We identified approximately 3 million single-nucleotide polymorphisms (SNPs) inside this region, of which 13.6% were not in the dbSNP database. Genotyping analysis showed that SNP identification had high accuracy and consistency, indicating the high sequence quality of this assembly. We also carried out heterozygote phasing and haplotype prediction against HapMap CHB and JPT haplotypes (Chinese and Japanese, respectively), sequence comparison with the two available individual genomes (J. D. Watson and J. C. Venter), and structural variation identification. These variations were considered for their potential biological impact. Our sequence data and analyses demonstrate the potential usefulness of next-generation sequencing technologies for personal genomics

Extraction, purification, biological effects and applications of acrosin: a review

Acrosin is a proteolytic enzyme in the sperm acrosome that can stimulate sperm to penetrate the zona pellucida, causing the fertilization of the oocyte. Its activity is a crucial indicator of the sperm’s fertilization ability, which is critical in mammalian and human reproduction. However, there exists a lack of a comprehensive review of acrosin. In this study, we compared the extraction methods of acrosin, including acid extraction, buffer extraction, and saline extraction. The main methods for purifying acrosin, such as ammonium sulfate precipitation, ultrafiltration, dialysis, gel filtration chromatography, ion-exchange chromatography, and affinity chromatography, are reviewed. In addition, a detailed overview of the biological functions, inhibitors and applications of acrosin are outlined. This study provides methods for the extraction and purification of acrosin and some theoretical basis for the study of its properties. This provides a reference for further research on acrosin

Diffuse X-ray Explorer: a high-resolution X-ray spectroscopic sky surveyor on the China Space Station

DIffuse X-ray Explorer (DIXE) is a proposed high-resolution X-ray spectroscopic sky surveyor on the China Space Station (CSS). DIXE will focus on studying hot baryons in the Milky Way. Galactic hot baryons like the X-ray emitting Milky Way halo and eROSITA bubbles are best observed in the sky survey mode with a large field of view. DIXE will take advantage of the orbital motion of the CSS to scan a large fraction of the sky. High-resolution X-ray spectroscopy, enabled by superconducting microcalorimeters based on the transition-edge sensor (TES) technology, will probe the physical properties (e.g., temperature, density, elemental abundances, kinematics) of the Galactic hot baryons. This will complement the high-resolution imaging data obtained with the eROSITA mission. Here we present the preliminary design of DIXE. The payload consists mainly of a detector assembly and a cryogenic cooling system. The key components of the detector assembly are a microcalorimeter array and frequency-domain multiplexing readout electronics. To provide a working temperature for the detector assembly, the cooling system consists of an adiabatic demagnetization refrigerator and a mechanical cryocooler system.Comment: 12 pages, 6 figures, the full version is published by Journal of Low Temperature Physic

Machine learning potential predictor of idiopathic pulmonary fibrosis

IntroductionIdiopathic pulmonary fibrosis (IPF) is a severe chronic respiratory disease characterized by treatment challenges and poor prognosis. Identifying relevant biomarkers for effective early-stage risk prediction is therefore of critical importance.MethodsIn this study, we obtained gene expression profiles and corresponding clinical data of IPF patients from the GEO database. GO enrichment and KEGG pathway analyses were performed using R software. To construct an IPF risk prediction model, we employed LASSO-Cox regression analysis and the SVM-RFE algorithm. PODNL1 and PIGA were identified as potential biomarkers associated with IPF onset, and their predictive accuracy was confirmed using ROC curve analysis in the test set. Furthermore, GSEA revealed enrichment in multiple pathways, while immune function analysis demonstrated a significant correlation between IPF onset and immune cell infiltration. Finally, the roles of PODNL1 and PIGA as biomarkers were validated through in vivo and in vitro experiments using qRT-PCR, Western blotting, and immunohistochemistry.ResultsThese findings suggest that PODNL1 and PIGA may serve as critical biomarkers for IPF onset and contribute to its pathogenesis.DiscussionThis study highlights their potential for early biomarker discovery and risk prediction in IPF, offering insights into disease mechanisms and diagnostic strategies

A study of multinucleated giant cells in esophageal cancer

Objectives: To evaluate the occurrence, abundance, distribution, nature and clinical significance of multi-nucleated giant cell (MGC) in esophageal cancer. Materials and methods: MGCs were examined with conventional pathology, immunohistochemistry and immunofluorescence in 107 esophageal cancer tissues. The findings were correlated to pathological diagnosis and clinical behavior of the cancers. Results: MGCs were identified in 31.7% (34/107) of the cases. MGCs were positive for CD11c, CD11b, CD32, CD16, HLA-DR and MMP9, and negative for CD163, CD206 and CD64 giving a molecular profile of proinflammatory M1 but not immunosuppressive M2. MGCs were significantly related to decreased lymph node metastasis (p = 0.011), low pTNM stage (p = 0.044), favorable survival (p = 0.04), squamous cell cancer type rather than other histopathological subtypes (p = 0.020) and associated to better differentiation (p = 0.063). Conclusions: MGCs belong to M1 macrophage and perform phagocytosis and scavenging of cancer cells that would benefit patients' survival and could serve as a prognostic marker

Tumor treating fields in glioblastoma: long-term treatment and high compliance as favorable prognostic factors

IntroductionTumor treating fields (TTFields) have earned substantial attention in recent years as a novel therapeutic approach with the potential to improve the prognosis of glioblastoma (GBM) patients. However, the impact of TTFields remains a subject of ongoing debate. This study aimed to offer real-world evidence on TTFields therapy for GBM, and to investigate the clinical determinants affecting its efficacy.MethodsWe have reported a retrospective analysis of 81 newly diagnosed Chinese GBM patients who received TTFields/Stupp treatment in the Second Affiliated Hospital of Zhejiang University. Overall survival (OS) and progression-free survival (PFS) were analyzed using Kaplan–Meier method. Cox regression models with time-dependent covariates were utilized to address non-proportional hazards and to assess the influence of clinical variables on PFS and OS.ResultsThe median PFS and OS following TTFields/STUPP treatment was 12.6 months (95% CI 11.0-14.1) and 21.3 months (95% CI 10.0–32.6) respectively. Long-term TTFields treatment (>2 months) exhibits significant improvements in PFS and OS compared to the short-term treatment group (≤2 months). Time-dependent covariate COX analysis revealed that longer TTFields treatment was correlated with enhanced PFS and OS for up to 12 and 13 months, respectively. Higher compliance to TTFields (≥ 0.8) significantly reduced the death risk (HR=0.297, 95%CI 0.108-0.819). Complete surgical resection and MGMT promoter methylation were associated with significantly lower risk of progression (HR=0.337, 95% CI 0.176-0.643; HR=0.156, 95% CI 0.065-0.378) and death (HR=0.276, 95% CI 0.105-0.727; HR=0.249, 95% CI 0.087-0.710).ConclusionThe TTFields/Stupp treatment may prolong median OS and PFS in GBM patients, with long-term TTFields treatment, higher TTFields compliance, complete surgical resection, and MGMT promoter methylation significantly improving prognosis