Construction of high-density genetic map based on SLAF-seq and QTL analysis of major traits in sweetpotato [Ipomoea batatas (L.) Lam.].

Sweetpotato, Ipomoea batatas (L.) Lam., is an important worldwide crop used as feed, food, and fuel. However, its polyploidy, high heterozygosity and self-incompatibility makes it difficult to study its genetics and genomics. Longest vine length (LVL), yield per plant (YPP), dry matter content (DMC), starch content (SC), soluble sugar content (SSC), and carotenoid content (CC) are some of the major agronomic traits being used to evaluate sweetpotato. However limited research has actually examined how these traits are inherited. Therefore, after selecting 212 F1 from a Xin24 × Yushu10 crossing as the mapping population, this study applied specific-locus amplified fragment sequencing (SLAF-seq), at an average sequencing depth of 26.73 × (parents) and 52.25 × (progeny), to detect single nucleotide polymorphisms (SNPs). This approach generated an integrated genetic map of length 2441.56 cM and a mean distance of 0.51 cM between adjacent markers, encompassing 15 linkage groups (LGs). Based on the linkage map, 26 quantitative trait loci (QTLs), comprising six QTLs for LVL, six QTLs for YPP, ten QTLs for DMC, one QTL for SC, one QTL for SSC, and two QTLs for CC, were identified. Each of these QTLs explained 6.3-10% of the phenotypic variation. It is expected that the findings will be of benefit for marker-assisted breeding and gene cloning of sweetpotato.

Association of Metal Exposure with Novel Immunoinflammatory Indicators.

Objective: We aimed to investigate the relationship between metal exposure and novel immunoinflammatory indicators. Methods: Data on adults participating in the National Health and Nutrition Examination Survey (NHANES) from 2009 to 2018 were analyzed. Various statistical models were employed to assess the association between metal exposure and novel immune-inflammation-related indicators. Additionally, the impact of metal exposure on inflammation in different gender populations was explored. Results: This study included 4482 participants, of whom 51.1% were male. Significant correlations were observed among various metals. Both elastic net (ENET) and linear regression models revealed robust associations between cadmium (Cd), cobalt (Co), arsenic (As), mercury (Hg), and immunoinflammatory indicators. Weighted quantile sum (WQS) and Quantile g-computation (Q-gcomp) models demonstrated strong associations between barium (Ba), Co, and Hg and immunoinflammatory indicators. Bayesian kernel machine regression (BKMR) analysis indicated an overall positive correlation between in vivo urinary metal levels and systemic inflammation response index (SIRI) and aggregate index of systemic inflammation (AISI). Furthermore, Co, As, and Hg emerged as key metals contributing to changes in novel immunoinflammatory indicators. Conclusions: Metals exhibit associations with emerging immunoinflammatory indicators, and concurrent exposure to mixed metals may exacerbate the inflammatory response. Furthermore, this relationship varies across gender populations.

Disorder-Broadened Phase Boundary with Enhanced Amorphous Superconductivity in Pressurized In2Te5.

As an empirical tool in materials science and engineering, the iconic phase diagram owes its robustness and practicality to the topological characteristics rooted in the celebrated Gibbs phase law free variables (F) = components (C) - phases (P) + 2. When crossing the phase diagram boundary, the structure transition occurs abruptly, bringing about an instantaneous change in physical properties and limited controllability on the boundaries (F = 1). Here, the sharp phase boundary is expanded to an amorphous transition region (F = 2) by partially disrupting the long-range translational symmetry, leading to a sequential crystalline-amorphous-crystalline (CAC) transition in a pressurized In2Te5 single crystal. Through detailed in situ synchrotron diffraction, it is elucidated that the phase transition stems from the rotation of immobile blocks [In2Te2]2+, linked by hinge-like [Te3]2- trimers. Remarkably, within the amorphous region, the amorphous phase demonstrates a notable 25% increase of the superconducting transition temperature (Tc), while the carrier concentration remains relatively constant. Furthermore, a theoretical framework is proposed revealing that the unconventional boost in amorphous superconductivity might be attributed to an intensified electron correlation, triggered by a disorder-augmented multifractal behavior. These findings underscore the potential of disorder and prompt further exploration of unforeseen phenomena on the phase boundaries.

Potential Application Prospects of Biomolecule-Modified Two-Dimensional Chiral Nanomaterials in Biomedicine.

Chirality, one of the most fundamental properties of natural molecules, plays a significant role in biochemical reactions. Nanomaterials with chiral characteristics have superior properties, such as catalytic properties, optoelectronic properties, and photothermal properties, which have significant potential for specific applications in nanomedicine. Biomolecular modifications such as nucleic acids, peptides, proteins, and polysaccharides are sources of chirality for nanomaterials with great potential for application in addition to intrinsic chirality, artificial macromolecules, and metals. Two-dimensional (2D) nanomaterials, as opposed to other dimensions, due to proper surface area, extensive modification sites, drug loading potential, and simplicity of preparation, are prepared and utilized in diagnostic applications, drug delivery research, and tumor therapy. Current advanced studies on 2D chiral nanomaterials for biomedicine are focused on novel chiral development, structural control, and materials sustainability applications. However, despite the advances in biomedical research, chiral 2D nanomaterials still confront challenges such as the difficulty of synthesis, quality control, batch preparation, chiral stability, and chiral recognition and selectivity. This review aims to provide a comprehensive overview of the origins, synthesis, applications, and challenges of 2D chiral nanomaterials with biomolecules as cargo and chiral modifications and highlight their potential roles in biomedicine.

Meningioma consistency assessment based on the fusion of deep learning features and radiomics features.

PURPOSE: This study aims to combine deep learning features with radiomics features for the computer-assisted preoperative assessment of meningioma consistency. METHODS: 202 patients with surgery and pathological diagnosis of meningiomas at our institution between December 2016 and December 2018 were retrospectively included in the study. The T2-fluid attenuated inversion recovery (T2-Flair) images were evaluated to classify meningioma as soft or hard by professional neurosurgeons based on Zada's consistency grading system. All the patients were split randomly into a training cohort (n = 162) and a testing cohort (n = 40). A convolutional neural network (CNN) model was proposed to extract deep learning features. These deep learning features were combined with radiomics features. After multiple feature selections, selected features were used to construct classification models using four classifiers. AUC was used to evaluate the performance of each classifier. A signature was further constructed by using the least absolute shrinkage and selection operator (LASSO). A nomogram based on the signature was created for predicting meningioma consistency. RESULTS: The logistic regression classifier constructed using 17 radiomics features and 9 deep learning features provided the best performance with a precision of 0.855, a recall of 0.854, an F1-score of 0.852 and an AUC of 0.943 (95 % CI, 0.873-1.000) in the testing cohort. The C-index of the nomogram was 0.822 (95 % CI, 0.758-0.885) in the training cohort and 0.943 (95 % CI, 0.873-1.000) in the testing cohort with good calibration. Decision curve analysis further confirmed the clinical usefulness of the nomogram for predicting meningioma consistency. CONCLUSIONS: The proposed method for assessing meningioma consistency based on the fusion of deep learning features and radiomics features is potentially clinically valuable. It can be used to assist physicians in the preoperative determination of tumor consistency.

Astrocyte-derived lactoferrin reduces ß-amyloid burden by promoting the interaction between p38 kinase and PP2A phosphatase in male APP/PS1 transgenic mice.

BACKGROUND AND PURPOSE: Overexpression of astrocytic lactoferrin (Lf) was observed in the brain of Alzheimer's disease (AD) patients, whereas the role of astrocytic Lf in AD progression remains unexplored. In this study, we aimed to evaluate the effects of astrocytic Lf on AD progression. EXPERIMENTAL APPROACH: Male APP/PS1 mice with astrocytes overexpressing human Lf were developed to evaluate the effects of astrocytic Lf on AD progression. N2a-sw cells also were employed to further uncover the mechanism of astrocytic Lf on ß-amyloid (Aß) production. KEY RESULTS: Astrocytic Lf overexpression increased protein phosphatase 2A (PP2A) activity and reduced amyloid precursor protein (APP) phosphorylation, Aß burden and tau hyperphosphorylation in APP/PS1 mice. Mechanistically, astrocytic Lf overexpression promoted the uptake of astrocytic Lf into neurons in APP/PS1 mice, and conditional medium from astrocytes overexpressing Lf inhibited p-APP (Thr668) expression in N2a-sw cells. Furthermore, recombinant human Lf (hLf) significantly enhanced PP2A activity and inhibited p-APP expression, whereas inhibition of p38 or PP2A activities abrogated the hLf-induced p-APP down-regulation in N2a-sw cells. Additionally, hLf promoted the interaction of p38 and PP2A via p38 activation, thereby enhancing PP2A activity, and low-density lipoprotein receptor-related protein 1 (LRP1) knockdown significantly reversed the hLf-induced p38 activation and p-APP down-regulation. CONCLUSIONS AND IMPLICATIONS: Our data suggested that astrocytic Lf promoted neuronal p38 activation, via targeting to LRP1, subsequently promoting p38 binding to PP2A to enhance PP2A enzyme activity, which finally inhibited Aß production via APP dephosphorylation. In conclusion, promoting astrocytic Lf expression may be a potential strategy against AD. LINKED ARTICLES: This article is part of a themed issue From Alzheimer's Disease to Vascular Dementia: Different Roads Leading to Cognitive Decline. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.6/issuetoc.

From zinc homeostasis to disease progression: Unveiling the neurodegenerative puzzle.

Zinc is a crucial trace element in the human body, playing a role in various physiological processes such as oxidative stress, neurotransmission, protein synthesis, and DNA repair. The zinc transporters (ZnTs) family members are responsible for exporting intracellular zinc, while Zrt- and Irt-like proteins (ZIPs) are involved in importing extracellular zinc. These processes are essential for maintaining cellular zinc homeostasis. Imbalances in zinc metabolism have been linked to the development of neurodegenerative diseases. Disruptions in zinc levels can impact the survival and activity of neurons, thereby contributing to the progression of neurodegenerative diseases through mechanisms like cell apoptosis regulation, protein phase separation, ferroptosis, oxidative stress, and neuroinflammation. Therefore, conducting a systematic review of the regulatory network of zinc and investigating the relationship between zinc dysmetabolism and neurodegenerative diseases can enhance our understanding of the pathogenesis of these diseases. Additionally, it may offer new insights and approaches for the treatment of neurodegenerative diseases.

Multicenter Study of the Utility of Convolutional Neural Network and Transformer Models for the Detection and Segmentation of Meningiomas.

PURPOSE: This study aimed to investigate the effectiveness and practicality of using models like convolutional neural network and transformer in detecting and precise segmenting meningioma from magnetic resonance images. METHODS: The retrospective study on T1-weighted and contrast-enhanced images of 523 meningioma patients from 3 centers between 2010 and 2020. A total of 373 cases split 8:2 for training and validation. Three independent test sets were built based on the remaining 150 cases. Six convolutional neural network detection models trained via transfer learning were evaluated using 4 metrics and receiver operating characteristic analysis. Detected images were used for segmentation. Three segmentation models were trained for meningioma segmentation and were evaluated via 4 metrics. In 3 test sets, intraclass consistency values were used to evaluate the consistency of detection and segmentation models with manually annotated results from 3 different levels of radiologists. RESULTS: The average accuracies of the detection model in the 3 test sets were 97.3%, 93.5%, and 96.0%, respectively. The model of segmentation showed mean Dice similarity coefficient values of 0.884, 0.834, and 0.892, respectively. Intraclass consistency values showed that the results of detection and segmentation models were highly consistent with those of intermediate and senior radiologists and lowly consistent with those of junior radiologists. CONCLUSIONS: The proposed deep learning system exhibits advanced performance comparable with intermediate and senior radiologists in meningioma detection and segmentation. This system could potentially significantly improve the efficiency of the detection and segmentation of meningiomas.

Detection of Image Artifacts Using Improved Cascade Region-Based CNN for Quality Assessment of Endoscopic Images.

Endoscopy is a commonly used clinical method for gastrointestinal disorders. However, the complexity of the gastrointestinal environment can lead to artifacts. Consequently, the artifacts affect the visual perception of images captured during endoscopic examinations. Existing methods to assess image quality with no reference display limitations: some are artifact-specific, while others are poorly interpretable. This study presents an improved cascade region-based convolutional neural network (CNN) for detecting gastrointestinal artifacts to quantitatively assess the quality of endoscopic images. This method detects eight artifacts in endoscopic images and provides their localization, classification, and confidence scores; these scores represent image quality assessment results. The artifact detection component of this method enhances the feature pyramid structure, incorporates the channel attention mechanism into the feature extraction process, and combines shallow and deep features to improve the utilization of spatial information. The detection results are further used for image quality assessment. Experimental results using white light imaging, narrow-band imaging, and iodine-stained images demonstrate that the proposed artifact detection method achieved the highest average precision (62.4% at a 50% IOU threshold). Compared to the typical networks, the accuracy of this algorithm is improved. Furthermore, three clinicians validated that the proposed image quality assessment method based on the object detection of endoscopy artifacts achieves a correlation coefficient of 60.71%.

Screening and optimisation of in vitro pollen germination medium for sweetpotato (Ipomoea batatas).

BACKGROUND: Sweetpotato is an important vegetable and food crop that is bred through sexual crosses and systematic selection. The use of in vitro germination of sweetpotato pollen to test its viability has important theoretical and practical implications for improving the efficiency of sweetpotato crossbreeding by controlling pollination and conducting research on sweetpotato pollen biology. RESULTS: In this study, we observed the morphological structure of sweetpotato pollen under a scanning electron microscope (SEM), developed an effective method for the in vitro germination of sweetpotato pollen, and examined the viability of sweetpotato pollen after treating plants at different temperatures before blossoming. Sweetpotato pollen grains are spherical, with an average diameter of 87.07 ± 3.27 µm (excluding spines), with multiple germination pores and reticulate pollen surface sculpture. We applied numerous media to sweetpotato pollen germination in vitro to screen the initial medium and optimised the medium components through single-factor design. The most effective liquid medium for in vitro sweetpotato pollen germination contained 50 g/L Sucrose, 50 g/L Polyethylene glycol 4000 (PEG4000), 100 mg/L Boric acid and 300 mg/L Calcium nitrate, with a pH = 6.0. The optimum growth temperature for pollen development in sweetpotato was from 25 to 30 °C. Neither staining nor in situ germination could accurately determine the viability of sweetpotato pollen. CONCLUSIONS: In vitro germination can be used to effectively determine sweetpotato pollen viability. The best liquid medium for in vitro germination of sweetpotato pollen contained 50 g/L Sucrose, 50 g/L Polyethylene glycol 4000 (PEG4000), 100 mg/L Boric acid and 300 mg/L Calcium nitrate, with the pH adjusted to 6.0. This study provides a reliable medium for the detection of sweetpotato pollen viability, which can provide a theoretical reference for sweetpotato genetics and breeding.

Graphdiyne nanoplatforms for photothermal-ferroptosis combination therapy against glioblastoma.

Glioblastoma (GBM) is one of the most malignant tumors of the central nervous system and has a poor prognosis. GBM cells are highly sensitive to ferroptosis and heat, suggesting thermotherapy-ferroptosis as a new strategy for GBM treatment. With its biocompatibility and photothermal conversion efficiency, graphdiyne (GDY) has become a high-profile nanomaterial. Here, the ferroptosis inducer FIN56 was employed to construct GDY-FIN56-RAP (GFR) polymer self-assembled nanoplatforms against GBM. GDY could effectively load FIN56 and FIN56 released from GFR in a pH-dependent manner. The GFR nanoplatforms possessed the advantages of penetrating the BBB and acidic environment-induced in situ FIN56 release. Moreover, GFR nanoplatforms induced GBM cell ferroptosis by inhibiting GPX4 expression, and 808 nm irradiation reinforced GFR-mediated ferroptosis by elevating the temperature and promoting FIN56 release from GFR. In addition, the GFR nanoplatforms were inclined to locate in tumor tissue, inhibit GBM growth, and prolong lifespan by inducing GPX4-mediated ferroptosis in an orthotopic xenograft mouse model of GBM; meanwhile, 808 nm irradiation further improved these GFR-mediated effects. Hence, GFR may be a potential nanomedicine for cancer therapy, and GFR combined with photothermal therapy may be a promising strategy against GBM.

Artificial intelligence assisted detection of superficial esophageal squamous cell carcinoma in white-light endoscopic images by using a generalized system.

BACKGROUND: The use of artificial intelligence (AI) assisted white light imaging (WLI) detection systems for superficial esophageal squamous cell carcinoma (SESCC) is limited by training with images from one specific endoscopy platform. METHODS: In this study, we developed an AI system with a convolutional neural network (CNN) model using WLI images from Olympus and Fujifilm endoscopy platforms. The training dataset consisted of 5892 WLI images from 1283 patients, and the validation dataset included 4529 images from 1224 patients. We assessed the diagnostic performance of the AI system and compared it with that of endoscopists. We analyzed the system's ability to identify cancerous imaging characteristics and investigated the efficacy of the AI system as an assistant in diagnosis. RESULTS: In the internal validation set, the AI system's per-image analysis had a sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of 96.64%, 95.35%, 91.75%, 90.91%, and 98.33%, respectively. In patient-based analysis, these values were 90.17%, 94.34%, 88.38%, 89.50%, and 94.72%, respectively. The diagnostic results in the external validation set were also favorable. The CNN model's diagnostic performance in recognizing cancerous imaging characteristics was comparable to that of expert endoscopists and significantly higher than that of mid-level and junior endoscopists. This model was competent in localizing SESCC lesions. Manual diagnostic performances were significantly improved with the assistance by AI system, especially in terms of accuracy (75.12% vs. 84.95%, p = 0.008), specificity (63.29% vs. 76.59%, p = 0.017) and PPV (64.95% vs. 75.23%, p = 0.006). CONCLUSIONS: The results of this study demonstrate that the developed AI system is highly effective in automatically recognizing SESCC, displaying impressive diagnostic performance, and exhibiting strong generalizability. Furthermore, when used as an assistant in the diagnosis process, the system improved manual diagnostic performance.

AI-assisted identification of intrapapillary capillary loops in magnification endoscopy for diagnosing early-stage esophageal squamous cell carcinoma: a preliminary study.

Esophageal squamous cell carcinoma (ESCC) is one of the most common histological types of esophageal cancers. It can seriously affect public health, particularly in Eastern Asia. Early diagnosis and effective therapy of ESCC can significantly help improve patient prognoses. The visualization of intrapapillary capillary loops (IPCLs) under magnification endoscopy (ME) can greatly support the identification of ESCC occurrences by endoscopists. This paper proposes an artificial-intelligence-assisted endoscopic diagnosis approach using deep learning for localizing and identifying IPCLs to diagnose early-stage ESCC. An improved Faster region-based convolutional network (R-CNN) with a polarized self-attention (PSA)-HRNetV2p backbone was employed to automatically detect IPCLs in ME images. In our study, 2887 ME with blue laser imaging (ME-BLI) images of 246 patients and 493 ME with narrow-band imaging (ME-NBI) images of 81 patients were collected from multiple hospitals and used to train and test our detection model. The ME-NBI images were used as the external testing set to verify the generalizability of the model. The experimental evaluation revealed that the proposed method achieved a recall of 79.25%, precision of 75.54%, F1-score of 0.764 and mean average precision (mAP) of 74.95%. Our method outperformed other existing approaches in our evaluation. It can effectively improve the accuracy of ESCC detection and provide a useful adjunct to the assessment of early-stage ESCC for endoscopists.

Resequencing of sweetpotato germplasm resources reveals key loci associated with multiple agronomic traits.

Sweetpotato is an important crop that exhibits hexaploidy and high heterozygosity, which limits gene mining for important agronomic traits. Here, 314 sweetpotato germplasm resources were deeply resequenced, and 4 599 509 SNPs and 846 654 InDels were generated, among which 196 124 SNPs were nonsynonymous and 9690 InDels were frameshifted. Based on the Indels, genome-wide marker primers were designed, and 3219 of 40 366 primer pairs were selected to construct the core InDel marker set. The molecular ID of 104 sweetpotato samples verified the availability of these primers. The sweetpotato population structures were then assessed through multiple approaches using SNPs, and diverse approaches demonstrated that population stratification was not obvious for most Chinese germplasm resources. As many as 20 important agronomic traits were evaluated, and a genome-wide association study was conducted on these traits. A total of 19 high-confidence loci were detected in both models. These loci included several candidate genes, such as IbMYB1, IbZEP1, and IbYABBY1, which might be involved in anthocyanin metabolism, carotenoid metabolism, and leaf morphogenesis, respectively. Among them, IbZEP1 and IbYABBY1 were first reported in sweetpotato. The variants in the promoter and the expression levels of IbZEP1 were significantly correlated with flesh color (orange or not orange) in sweetpotato. The expression levels of IbYABBY1 were also correlated with leaf shape. These results will assist in genetic and breeding studies in sweetpotato.

The metal ion hypothesis of Alzheimer's disease and the anti-neuroinflammatory effect of metal chelators.

Alzheimer's disease (AD), characterized by the ß-amyloid protein (Aß) deposition and tau hyperphosphorylation, is the most common dementia with uncertain etiology. The clinical trials of Aß monoclonal antibody drugs have almost failed, giving rise to great attention on the other etiologic hypothesis regarding AD such as metal ions dysmetabolism and chronic neuroinflammation. Mounting evidence revealed that the metal ions (iron, copper, and zinc) were dysregulated in the susceptible brain regions of AD patients, which was highly associated with Aß deposition, tau hyperphosphorylation, neuronal loss, as well as neuroinflammation. Further studies uncovered that iron, copper and zinc could not only enhance the production of Aß but also directly bind to Aß and tau to promote their aggregations. In addition, the accumulation of iron and copper could respectively promote ferroptosis and cuproptosis. Therefore, the metal ion chelators were recognized as promising agents for treating AD. This review comprehensively summarized the effects of metal ions on the Aß dynamics and tau phosphorylation in the progression of AD. Furthermore, taking chronic neuroinflammation contributes to the progression of AD, we also provided a summary of the mechanisms concerning metal ions on neuroinflammation and highlighted the metal ion chelators may be potential agents to alleviate neuroinflammation under the condition of AD. Nevertheless, more investigations regarding metal ions on neuroinflammation should be taken into practice, and the effects of metal ion chelators on neuroinflammation should gain more attention. Running title: Metal chelators against neuroinflammation.

Fast and Accurate Illumination Estimation Using LDR Panoramic Images for Realistic Rendering.

A high dynamic range (HDR) image is commonly used to reveal stereo illumination, which is crucial for generating high-quality realistic rendering effects. Compared to the high-cost HDR imaging technique, low dynamic range (LDR) imaging provides a low-cost alternative and is preferable for interactive graphics applications. However, the limited LDR pixel bit depth significantly bothers accurate illumination estimation using LDR images. The conflict between the realism and promptness of illumination estimation for realistic rendering is yet to be resolved. In this paper, an efficient method that accurately infers illuminations of real-world scenes using LDR panoramic images is proposed. It estimates multiple lighting parameters, including locations, types and intensities of light sources. In our approach, a new algorithm that extracts illuminant characteristics during the exposure attenuation process is developed to locate light sources and outline their boundaries. To better predict realistic illuminations, a new deep learning model is designed to efficiently parse complex LDR panoramas and classify detected light sources. Finally, realistic illumination intensities are calculated by recovering the inverse camera response function and extending the dynamic range of pixel values based on previously estimated parameters of light sources. The reconstructed radiance map can be used to compute high-quality image-based lighting of virtual models. Experimental results demonstrate that the proposed method is capable of efficiently and accurately computing comprehensive illuminations using LDR images. Our method can be used to produce better realistic rendering results than existing approaches.

On Using Classification Datasets to Evaluate Graph Outlier Detection: Peculiar Observations and New Insights.

It is common practice of the outlier mining community to repurpose classification datasets toward evaluating various detection models. To that end, often a binary classification dataset is used, where samples from one of the classes are designated as the "inlier" samples, and the other class is substantially down-sampled to create the (ground-truth) "outlier" samples. Graph-level outlier detection (GLOD) is rarely studied but has many potentially influential real-world applications. In this study, we identify an intriguing issue with repurposing graph classification datasets for GLOD. We find that ROC-AUC performance of the models changes significantly ("flips" from high to very low, even worse than random) depending on which class is down-sampled. Interestingly, ROC-AUCs on these two variants approximately sum to 1 and their performance gap is amplified with increasing propagations for a certain family of propagation-based outlier detection models. We carefully study the graph embedding space produced by propagation-based models and find two driving factors: (1) disparity between within-class densities, which is amplified by propagation, and (2) overlapping support (mixing of embeddings) across classes. We also study other graph embedding methods and downstream outlier detectors, and we find that the intriguing "performance flip" issue still widely exists but which version of the down-sample achieves higher performance may vary. Thoughtful analysis over comprehensive results further deepens our understanding of the established issue. With this study, we aim at drawing attention to this (to our knowledge) previously unnoticed issue for the rarely studied GLOD problem, and specifically to the following questions: (1) Given the performance flip issue we identified, where one version of the down-sample often yields worse-than-random performance, is it appropriate to evaluate GLOD by average performance across all down-sampled versions when repurposing graph classification datasets? (2) Considering consistently observed performance flip issue across different graph embedding methods we studied, is it possible to design better graph embedding methods to overcome the issue? We conclude the article with our insights to these questions.

Comparative Metabolomic and Transcriptomic Analyses of Phytochemicals in Two Elite Sweet Potato Cultivars for Table Use.

To elucidate nutritional components in sweet potato cultivars for table use and to compare the phytochemicals of cultivars from different countries, 'Kokei No. 14' and 'Xinxiang' were selected. The physiological parameters and metabolites were determined using the colorimetric method and widely targeted metabolomics, respectively. Transcriptomic analysis was performed to explain the mechanism that resulted in phytochemical differences. 'Xinxiang' showed higher flavonoid and carotenoid contents. Metabolomics showed five upregulated flavonoids. Two essential amino acids (EAAs) and one conditionally essential amino acid (CEAA) were upregulated, whereas four EAAs and two CEAAs were downregulated. Unlike lipids, in which only one of thirty-nine was upregulated, nine of twenty-seven differentially accumulated phenolic acids were upregulated. Three of the eleven different alkaloids were upregulated. Similarly, eight organic acids were downregulated, with two upregulated. In addition, three of the seventeen different saccharides and alcohols were upregulated. In 'other metabolites,' unlike vitamin C, 6'-O-Glucosylaucubin and pantetheine were downregulated. The differentially accumulated metabolites were enriched to pathways of the biosynthesis of secondary metabolites, ABC transporters, and tyrosine metabolism, whereas the differentially expressed genes were mainly concentrated in the metabolic pathway, secondary metabolite biosynthesis, and transmembrane transport functions. These results will optimize the sweet potato market structure and enable a healthier diet for East Asian residents.

A dual-branch hybrid dilated CNN model for the AI-assisted segmentation of meningiomas in MR images.

BACKGROUND AND OBJECTIVE: Treatment for meningiomas usually includes surgical removal, radiation therapy, and chemotherapy. Accurate segmentation of tumors significantly facilitates complete surgical resection and precise radiotherapy, thereby improving patient survival. In this paper, a deep learning model is constructed for magnetic resonance T1-weighted Contrast Enhancement (T1CE) images to develop an automatic processing scheme for accurate tumor segmentation. METHODS: In this paper, a novel Convolutional Neural Network (CNN) model is proposed for the accurate meningioma segmentation in MR images. It can extract fused features in multi-scale receptive fields of the same feature map based on MR image characteristics of meningiomas. The attention mechanism is added as a helpful addition to the model to optimize the feature information transmission. RESULTS AND CONCLUSIONS: The results were evaluated on two internal testing sets and one external testing set. Mean Dice Similarity Coefficient (DSC) values of 0.886, 0.851, and 0.874 are demonstrated, respectively. In this paper, a deep learning approach is proposed to segment tumors in T1CE images. Multi-center testing sets validated the effectiveness and generalization of the method. The proposed model demonstrates state-of-the-art tumor segmentation performance.

Shubnikov-de Haas oscillations and nontrivial topological states in Weyl semimetal candidate SmAlSi.

The RAlX (R = Light rare earth; X = Ge, Si) compounds, as a family of magnetic Weyl semimetal, have recently attracted growing attention due to the tunability of Weyl nodes and its interactions with diverse magnetism by rare-earth atoms. Here, we report the magnetotransport evidence and electronic structure calculations on nontrivial band topology of SmAlSi, a new member of this family. At low temperatures, SmAlSi exhibits large non-saturated magnetoresistance (MR) (as large as ∼5500% at 2 K and 48 T) and distinct Shubnikov-de Haas (SdH) oscillations. The field dependent MRs at 2 K deviate from the semiclassical (µ0H)2variation but follow the power-law relation MR∝(µ0H)mwith a crossover fromm∼ 1.52 at low fields (µ0H< 15 T) tom∼ 1 under high fields (µ0H> 18 T), which is attributed to the existence of Weyl points and electron-hole compensated characteristics with high mobility. From the analysis of SdH oscillations, two fundamental frequencies originating from the Fermi surface pockets with non-trivialπBerry phases and small cyclotron mass can be identified, this feature is supported by the calculated electronic band structures with two Weyl pockets near the Fermi level. Our study establishes SmAlSi as a paradigm for researching the novel topological states of RAlX family.