Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks.

BACKGROUND: Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples. METHODS: We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA. RESULTS: Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples. CONCLUSIONS: In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.

Evaluation of a multimodal ctDNA-based assay for detection of aggressive cancers lacking standard screening tests.

Aim: Cancers lacking standard screening (LSS) options account for approximately 70% of cancer-related deaths due to late-stage diagnosis. Circulating tumor DNA (ctDNA) is a promising biomarker for multi-cancer early detection. We previously developed SPOT-MAS, a multimodal ctDNA-based assay analyzing methylation and fragmentomic profiles, effective in detecting common cancers (breast, colorectal, liver, lung and gastric). This study extends the analysis to five LSS cancers: endometrial, esophageal, head and neck, ovarian and pancreatic.Methods: SPOT-MAS was applied to profile cfDNA methylation and fragmentomic patterns in 739 healthy individuals and 135 LSS cancer patients.Results: We identified 347 differentially methylated regions and observed genome-wide hypomethylation across all five LSS cancers. Esophageal and head and neck cancers showed an enrichment of short cfDNA fragments (<150 bp). Eleven 4-mer end motifs were consistently altered in cfDNA fragments across all LSS cancers. Many significant signatures were consistent with previous observations in common cancers. Notably, SPOT-MAS achieved 96.2% specificity and 74.8% overall sensitivity, with a lower sensitivity of 60.7% in early-stage cancers.Conclusion: This proof-of-concept study demonstrates that SPOT-MAS a non-invasive test trained on five common cancer types, could detect a number of LSS cancer cases, potentially complementing existing screening programs.

Many cancers do not have standard tests, so they are often found too late, which leads to about 70% of cancer deaths. We've created a blood test that can help find cancer early. This test has already worked well for common cancers like breast and lung cancer, and now we're testing it on five harder-to-detect cancers: endometrial, esophageal, head and neck, ovarian and pancreatic cancers. In our study, we tested our blood test on 739 healthy people and 135 patients with these difficult cancers. Our method correctly identified healthy people 96.2% of the time and found cancer cases 74.8% of the time. This new test could help with screening for types of cancer that do not have good tests right now.

Comparison between U-shaped structural deep learning models to detect landslide traces.

Landslides endanger lives and public infrastructure in mountainous areas. Monitoring landslide traces in real-time is difficult for scientists, sometimes costly and risky because of the harsh terrain and instability. Nowadays, modern technology may be able to identify landslide-prone locations and inform locals for hours or days when the weather worsens. This study aims to propose indicators to detect landslide traces on the fields and remote sensing images; build deep learning (DL) models to identify landslides from Sentinel-2 images automatically; and apply DL-trained models to detect this natural hazard in some particular areas of Vietnam. Nine DL models were trained based on three U-shaped architectures, including U-Net, U2-Net, and U-Net3+, and three options of input sizes. The multi-temporal Sentinel-2 images were chosen as input data for training all models. As a result, the U-Net, using an input image size of 32 × 32 and a performance of 97 % with a loss function of 0.01, can detect typical landslide traces in Vietnam. Meanwhile, the U-Net (64 × 64) can detect more considerable landslide traces. Based on multi-temporal remote sensing data, a different case study in Vietnam was chosen to see landslide traces over time based on the trained U-Net (32 × 32) model. The trained model allows mountain managers to track landslide occurrences during wet seasons. Thus, landslide incidents distant from residential areas may be discovered early to warn of flash floods.

[Neural bases of memory and spatial navigation]. / Bases neurales de la mémoire et de la navigation spatiale.

The cognitive map is a concept first introduced by Edward Tolman in 1948 to describe the map of the environment stored in the brain. In this review, after a brief mention of the history of this concept, we explore the contributions of place cells and grid cells to the neural basis of the creation and storage of a spatial map. Finally, we discuss how this map is consolidated and stored in the brain. Questioning and advancing our knowledge of the mechanisms of our memory is essential to improve healthy aging of these systems.

Title: Bases neurales de la mémoire et de la navigation spatiale. Abstract: La carte cognitive est un concept introduit pour la première fois par Edward Tolman en 1948 pour décrire la carte de l'environnement stockée dans le cerveau. Dans cette revue, après une brève évocation de l'histoire de ce concept, nous explorerons les contributions des cellules de lieu et des cellules de grille aux bases neurales de la création et de l'archivage de cette cartographie spatiale. Nous discuterons enfin de la façon dont cette carte est consolidée et stockée dans le cerveau. L'exploration toujours plus poussée des mécanismes de notre mémoire demeure essentielle pour espérer soutenir les adaptations naturelles qui sous-tendent la flexibilité de la cognition au cours de la vie.

Inclusion complexes of squalene with beta-cyclodextrin and methyl-beta-cyclodextrin: preparation and characterization.

The present work aimed to investigate inclusion complexes of squalene with various cyclodextrins (native ß-cyclodextrin and methyl-ß-cyclodextrin). The production of squalene-ß-cyclodextrin inclusion complex was obtained using Response Surface Methodology and obtained inclusion complexes were studied with FTIR spectroscopy, X-ray diffractometry, thermal analysis, and 1H-NMR spectrometry. At the same time, squalene content was determined by reversed-phase high-performance liquid chromatography. All results confirmed that squalene was successfully involved in the cyclodextrin cavities. Optimizing the condition in preparation for the squalen-ß-cyclodextrin inclusion complex yielded 54.3% with squalene content of 9.01%. The essential difference for the inclusion complex of squalene with methylated beta-cyclodextrin was that no precipitate formed in the initial mixture, and the complex was more efficiently dispersed in water. The conclusions of the inclusion complex formation were confirmed by computer simulation by optimizing the complex geometry using the DFT, MM2, and MP3 methods.

Alpinia nelumboides Nob.Tanaka, T.T.K.Van & V.Hoang: phytochemical analysis and antioxidant activities of pseudo-stem and rhizome essential oils.

Alpinia nelumboides Nob.Tanaka, T.T.K.Van & V.Hoang is the new Alpninia species discovered in Vietnam in 2023. Herein, we first hydrodistillated its pseudo-stems and rhizomes to obtain its essential oils, PS-EO and RH-EO. Their volatile compounds and total polyphenols were analysed by gas chromatography-mass spectrometry and the Folin-Ciocalteu method, respectively. Antioxidant activities were determined using four different approaches. The results showed that PS-EO and RH-EO contained 40 and 31 compounds, accounting for 99.78% and 99.45% of their compositions, respectively. The contents of polyphenols and monoterpenes in PS-EO were higher than in RH-EO. RH-EO displayed weaker scavenging activities (17.40-19.53%) than PS-EO (30.81-44.08%). PS-EO also showed higher ferric and cupric reducing powers, with EC50 values of 3.50-5.30 mg/mL smaller than RH-EO's EC50 values of 19.0-23.0 mg/mL. These results first revealed the phytochemical profile and antioxidant activities of EOs from A. nelumboides.

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization.

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.

Growth-Inhibiting, Bactericidal, Antibiofilm, and Urease Inhibitory Activities of Hibiscus rosa sinensis L. Flower Constituents toward Antibiotic Sensitive- and Resistant-Strains of Helicobacter pylori.

The aim of the present study was to assess antimicrobial effects of naringenin (NRG), luteolin (LUT), myricetin (MCT), and protocatechuic acid (PCA) identified in a Hibiscus rosa sinensis flower against two reference strains and five clinical isolates of Helicobacter pylori. NRG displayed the most growth inhibitory and bactericidal activities to seven bacterial strains including six strains resistant to one or several antibiotics, azithromycin (MIC, 16-32 mg/L), erythromycin (MIC, 32 mg/L), levofloxacin (MIC, 32 mg/L), and/or metronidazole (24-64 mg/L), followed by LUT and MCT, while PCA showed weak activities toward the strains. These constituents had similar antibacterial activities toward the seven tested strains suggesting that these constituents and the antibiotics do not have a common mechanism of anti-H. pylori activity. NRG, LUT, and MCT resulted in a high percentage of coccoid forms of H. pylori. NRG exhibited the highest anti-biofilm formation activity. MCT produced the strongest inhibition of urease activity followed by LUT and PCA, whereas the activity of NRG was similar to standard inhibitor thiourea. The four constituents had no significant toxicity to human cell lines. A global attempt to decrease utilization of antibiotics justifies the need for further research on H. rosa sinensis derived materials containing NRG, LUT, MCT, and PCA as potential products or lead compounds for the prevention or treatment of diseases caused by H. pylori infection.

A JND-Based Pixel-Domain Algorithm and Hardware Architecture for Perceptual Image Coding.

This paper presents a hardware efficient pixel-domain just-noticeable difference (JND) model and its hardware architecture implemented on an FPGA. This JND model architecture is further proposed to be part of a low complexity pixel-domain perceptual image coding architecture, which is based on downsampling and predictive coding. The downsampling is performed adaptively on the input image based on regions-of-interest (ROIs) identified by measuring the downsampling distortions against the visibility thresholds given by the JND model. The coding error at any pixel location can be guaranteed to be within the corresponding JND threshold in order to obtain excellent visual quality. Experimental results show the improved accuracy of the proposed JND model in estimating visual redundancies compared with classic JND models published earlier. Compression experiments demonstrate improved rate-distortion performance and visual quality over JPEG-LS as well as reduced compressed bit rates compared with other standard codecs such as JPEG 2000 at the same peak signal-to-perceptible-noise ratio (PSPNR). FPGA synthesis results targeting a mid-range device show very moderate hardware resource requirements and over 100 Megapixel/s throughput of both the JND model and the perceptual encoder.

Glassy carbon electrode modified with a graphene oxide/poly(o-phenylenediamine) composite for the chemical detection of hydrogen peroxide.

Conducting poly(o-phenylenediamine) (POPD)/graphene oxide (GO) composites were prepared using a facile and efficient method involving the in-situ polymerization of OPD in the presence of GO in an aqueous medium. Copper sulfate was used as an oxidative initiator for the polymerization of OPD. Scanning electron microscopy and transmission electron microscopy images showed that POPD microfibrils were formed and distributed relatively uniformly with GO sheets in the obtained composites. X-ray diffraction results revealed the highly crystal structure of POPD. This composite exhibited good catalytic activity and stability. These results highlight the potential applications of POPD/GO composites as excellent electrochemical sensors. The composites were used to modify glass carbon electrodes for the chemical detection of hydrogen peroxide in aqueous media.