TOTEM: a multi-cancer detection and localization approach using circulating tumor DNA methylation markers.

BACKGROUND: Detection of cancer and identification of tumor origin at an early stage improve the survival and prognosis of patients. Herein, we proposed a plasma cfDNA-based approach called TOTEM to detect and trace the cancer signal origin (CSO) through methylation markers. METHODS: We performed enzymatic conversion-based targeted methylation sequencing on plasma cfDNA samples collected from a clinical cohort of 500 healthy controls and 733 cancer patients with seven types of cancer (breast, colorectum, esophagus, stomach, liver, lung, and pancreas) and randomly divided these samples into a training cohort and a testing cohort. An independent validation cohort of 143 healthy controls, 79 liver cancer patients and 100 stomach cancer patients were recruited to validate the generalizability of our approach. RESULTS: A total of 57 multi-cancer diagnostic markers and 873 CSO markers were selected for model development. The binary diagnostic model achieved an area under the curve (AUC) of 0.907, 0.908 and 0.868 in the training, testing and independent validation cohorts, respectively. With a training specificity of 98%, the specificities in the testing and independent validation cohorts were 100% and 98.6%, respectively. Overall sensitivity across all cancer stages was 65.5%, 67.3% and 55.9% in the training, testing and independent validation cohorts, respectively. Early-stage (I and II) sensitivity was 50.3% and 45.7% in the training and testing cohorts, respectively. For cancer patients correctly identified by the binary classifier, the top 1 and top 2 CSO accuracies were 77.7% and 86.5% in the testing cohort (n = 148) and 76.0% and 84.0% in the independent validation cohort (n = 100). Notably, performance was maintained with only 21 diagnostic and 214 CSO markers, achieving a training AUC of 0.865, a testing AUC of 0.866, and an integrated top 2 accuracy of 83.1% in the testing cohort. CONCLUSIONS: TOTEM demonstrates promising potential for accurate multi-cancer detection and localization by profiling plasma methylation markers. The real-world clinical performance of our approach needs to be investigated in a much larger prospective cohort.

Genome-Scale Multimodal Analysis of Cell-Free DNA Whole-Methylome Sequencing for Noninvasive Esophageal Cancer Detection.

PURPOSE: Simultaneous profiling of cell-free DNA (cfDNA) methylation and fragmentation features to improve the performance of cfDNA-based cancer detection is technically challenging. We developed a method to comprehensively analyze multimodal cfDNA genomic features for more sensitive esophageal squamous cell carcinoma (ESCC) detection. MATERIALS AND METHODS: Enzymatic conversion-mediated whole-methylome sequencing was applied to plasma cfDNA samples extracted from 168 patients with ESCC and 251 noncancer controls. ESCC characteristic cfDNA methylation, fragmentation, and copy number signatures were analyzed both across the genome and at accessible cis-regulatory DNA elements. To distinguish ESCC from noncancer samples, a first-layer classifier was developed for each feature type, the prediction results of which were incorporated to construct the second-layer ensemble model. RESULTS: ESCC plasma genome displayed global hypomethylation, altered fragmentation size, and chromosomal copy number alteration. Methylation and fragmentation changes at cancer tissue-specific accessible cis-regulatory DNA elements were also observed in ESCC plasma. By integrating multimodal genomic features for ESCC detection, the ensemble model showed improved performance over individual modalities. In the training cohort with a specificity of 99.2%, the detection sensitivity was 81.0% for all stages and 70.0% for stage 0-II. Consistent performance was observed in the test cohort with a specificity of 98.4%, an all-stage sensitivity of 79.8%, and a stage 0-II sensitivity of 69.0%. The performance of the classifier was associated with the disease stage, irrespective of clinical covariates. CONCLUSION: This study comprehensively profiles the epigenomic landscape of ESCC plasma and provides a novel noninvasive and sensitive ESCC detection approach with genome-scale multimodal analysis.

Multimodal analysis of cell-free DNA whole-methylome sequencing for cancer detection and localization.

Multimodal epigenetic characterization of cell-free DNA (cfDNA) could improve the performance of blood-based early cancer detection. However, integrative profiling of cfDNA methylome and fragmentome has been technologically challenging. Here, we adapt an enzyme-mediated methylation sequencing method for comprehensive analysis of genome-wide cfDNA methylation, fragmentation, and copy number alteration (CNA) characteristics for enhanced cancer detection. We apply this method to plasma samples of 497 healthy controls and 780 patients of seven cancer types and develop an ensemble classifier by incorporating methylation, fragmentation, and CNA features. In the test cohort, our approach achieves an area under the curve value of 0.966 for overall cancer detection. Detection sensitivity for early-stage patients achieves 73% at 99% specificity. Finally, we demonstrate the feasibility to accurately localize the origin of cancer signals with combined methylation and fragmentation profiling of tissue-specific accessible chromatin regions. Overall, this proof-of-concept study provides a technical platform to utilize multimodal cfDNA features for improved cancer detection.

Iron colloidal transport mechanisms and sequestration of As, Ni, and Cu along AMD-induced environmental gradients.

Colloids are common in mine waters and their chemistry and interactions are critical aspects of metal(loid)s cycling. Previous studies mostly focus on the colloidal transport of metal(loid)s in zones where rivers and soil profiles receive acid mine drainage (AMD). However, there is limited knowledge of the colloid and the associated toxic element behavior as the effluent flows through the coal waste dump, where a geochemical gradient is produced due to AMD reacting with waste rocks which have high acid-neutralization effects. Here, we investigated the geochemistry of Fe and co-occurring elements As, Ni, and Cu along the coal waste dump, in aqueous, colloidal, and precipitate phases, using micro/ultrafiltration combined with STEM, AFM-nanoIR, SEM-EDS, XRD, and FTIR analysis. The results demonstrated that a fast attenuation of H+, SO42-, and metal(loid)s happened as the effluent flowed through the waste-rock dump. The Fe, As, Ni, and Cu were distributed across all colloidal sizes and primarily transported in the nano-colloidal phase (3 kDa-0.1 µm). An increasing pH induced a higher percentage of large Fe colloid fractions (> 0.1 µm) associated with greater sequestration of trace metals, and the values for As from 39.5 % to 54.4 %, Ni from 40.8 % to 75.7 %, and Cu from 43.7 % to 56.0 %, respectively. The Fe-bearing colloids in AMD upstream (pH ≤ 3.0) were primarily composed of Fe-O-S and Fe-O-C with minor Al-Si-O and Ca-O-S, while in less acidic and alkaline sections (pH ≥ 4.1), they were composed of Fe-O with minor Ca-O-S. The iron colloid agglomerates associated with As, Ni, and Cu precipitated coupling the transformation of jarosite, and schwertmannite to ferrihydrite, goethite, and gypsum. These results demonstrate that the formation and transformation of Fe-bearing colloids response to this unique geochemical gradient help to understand the natural metal(loid)s attenuation along the coal waste dump.

An investigation of the flexural behaviour of large-span prestressed and steel-reinforced concrete slabs.

The prestressed and steel-reinforced concrete slab (PSRCS) is an innovative composite structural member offering high load capacity and stiffness and exceptional anti-crack performance, making it a leading trend in composite structures. This paper presents the derived calculation formulas for bearing capacity, section stiffness, mid-span deflection of PSRCS. Additionally, a numerical analysis of PSRCS is conducted using ABAQUS software, with several models created to systematically investigate bearing capacity, section stiffness, anti-crack performance, and failure mode. Concurrently, PSRCS member parameters are analyzed for optimal design, and the results of finite element (FE) calculations are compared with theoretical formula calculations. The results demonstrate that PSRCS exhibits superior load capacity, section stiffness, and anti-crack performance comparing to conventional slabs. The parametric analysis offers optimal design for each parameter and presents the corresponding recommended span-to-depth ratios for various spans in PSRCS applications.

Deep-learning based broadband reflection reduction metasurface.

Reflection reduction metasurface (RRM) has been drawing much attention due to its potential application in stealth technology. However, the traditional RRM is designed mainly based on trial-and-error approaches, which is time-consuming and leads to inefficiency. Here, we report the design of a broadband RRM based on deep-learning methodology. On one hand, we construct a forward prediction network that can forecast the polarization conversion ratio (PCR) of the metasurface in a millisecond, demonstrating a higher efficiency than traditional simulation tools. On the other hand, we construct an inverse network to immediately derive the structure parameters once a target PCR spectrum is given. Thus, an intelligent design methodology of broadband polarization converters has been established. When the polarization conversion units are arranged in chessboard layout with 0/1 form, a broadband RRM is achieved. The experimental results show that the relative bandwidth reaches 116% (reflection<-10 dB) and 107.4% (reflection<-15 dB), which demonstrates a great advantage in bandwidth compared with the previous designs.

Pan-cancer landscape of abnormal ctDNA methylation across human tumors.

BACKGROUND: The aim of this paper is to explore the correlation between circulating tumor DNA (ctDNA) methylation and mutations and its value in clinical early cancer screening. METHODS: We performed target region methylation sequencing and genome sequencing on plasma samples. Methylation models to distinguish cancer from healthy individuals have been developed using hypermethylated genes in tumors and validated in training set and prediction set. RESULTS: We found that patients with cancer had higher levels of ctDNA methylation compared to healthy individuals. The level of ctDNA methylation in cell cycle, p53, Notch pathway in pan-cancer was significantly correlated with the number of mutations, and mutation frequency. Methylation burden in some tumors was significantly correlated with tumor mutational burden (TMB), microsatellite instability (MSI) and PD-L1. The ctDNA methylation differences in cancer patients were mainly concentrated in the Herpes simplex virus 1 infection pathway. The area under curve (AUC) of the training and prediction sets of the methylation model distinguishing cancer from healthy individuals were 0.93 and 0.92, respectively. CONCLUSION: Our study provides a landscape of methylation levels of important pathways in pan-cancer. ctDNA methylation significantly correlates with mutation type, frequency and number, providing a reference for clinical application of ctDNA methylation in early cancer screening.

Thermal Cloak: Theory, Experiment and Application.

In the past two decades, owing to the development of metamaterials and the theoretical tools of transformation optics and the scattering cancellation method, a plethora of unprecedented functional devices, especially invisibility cloaks, have been experimentally demonstrated in various fields, e.g., electromagnetics, acoustics, and thermodynamics. Since the first thermal cloak was theoretically reported in 2008 and experimentally demonstrated in 2012, great progress has been made in both theory and experiment. In this review, we report the recent advances in thermal cloaks, including the theoretical designs, experimental realizations, and potential applications. The three areas are classified according to the different mechanisms of heat transfer, namely, thermal conduction, thermal convection, and thermal radiation. We also provide an outlook toward the challenges and future directions in this fascinating area.

A Noninvasive Multianalytical Approach for Lung Cancer Diagnosis of Patients with Pulmonary Nodules.

Addressing the high false-positive rate of conventional low-dose computed tomography (LDCT) for lung cancer diagnosis, the efficacy of incorporating blood-based noninvasive testing for assisting practicing clinician's decision making in diagnosis of pulmonary nodules (PNs) is investigated. In this prospective observative study, next generation sequencing- (NGS-) based cell-free DNA (cfDNA) mutation profiling, NGS-based cfDNA methylation profiling, and blood-based protein cancer biomarker testing are performed for patients with PNs, who are diagnosed as high-risk patients through LDCT and subsequently undergo surgical resections, with tissue sections pathologically examined and classified. Using pathological classification as the gold standard, statistical and machine learning methods are used to select molecular markers associated with tissue's malignant classification based on a 98-patient discovery cohort (28 benign and 70 malignant), and to construct an integrative multianalytical model for tissue malignancy prediction. Predictive models based on individual testing platforms have shown varying levels of performance, while their final integrative model produces an area under the receiver operating characteristic curve (AUC) of 0.85. The model's performance is further confirmed on a 29-patient independent validation cohort (14 benign and 15 malignant, with power > 0.90), reproducing AUC of 0.86, which translates to an overall sensitivity of 80% and specificity of 85.7%.

Experimental demonstration of an ultra-thin radar-infrared bi-stealth rasorber.

We propose a radar-infrared bi-stealth rasorber that not only provides broad microwave absorptivity and low infrared emissivity but also possesses a microwave transmission window at low frequency. It is composed of three functional layers, which are carefully designed to independently control the infrared emission, microwave absorption, and transmission, respectively. The structure exhibits broadband (8.1-19.3 GHz) and high-efficiency (>90%) absorption. A transmission window appears at low frequency with a transmission peak of 80% at 2.68 GHz. The thermal emissivity of the structure is about 0.27 in the atmosphere window, which is close to that of metal. Moreover, the total thickness of the proposed structure is only 3.713 mm. The low-infrared-emissivity, high-microwave-absorption and frequency-selective-transmission properties promise it will find potential applications in various stealth fields.