Development of a CT image analysis-based scoring system to differentiate gastric schwannomas from gastrointestinal stromal tumors

PurposeTo develop a point-based scoring system (PSS) based on contrast-enhanced computed tomography (CT) qualitative and quantitative features to differentiate gastric schwannomas (GSs) from gastrointestinal stromal tumors (GISTs).MethodsThis retrospective study included 51 consecutive GS patients and 147 GIST patients. Clinical and CT features of the tumors were collected and compared. Univariate and multivariate logistic regression analyses using the stepwise forward method were used to determine the risk factors for GSs and create a PSS. Area under the receiver operating characteristic curve (AUC) analysis was performed to evaluate the diagnostic efficiency of PSS.ResultsThe CT attenuation value of tumors in venous phase images, tumor-to-spleen ratio in venous phase images, tumor location, growth pattern, and tumor surface ulceration were identified as predictors for GSs and were assigned scores based on the PSS. Within the PSS, GS prediction probability ranged from 0.60% to 100% and increased as the total risk scores increased. The AUC of PSS in differentiating GSs from GISTs was 0.915 (95% CI: 0.874–0.957) with a total cutoff score of 3.0, accuracy of 0.848, sensitivity of 0.843, and specificity of 0.850.ConclusionsThe PSS of both qualitative and quantitative CT features can provide an easy tool for radiologists to successfully differentiate GS from GIST prior to surgery

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery

Normalization of Web of Science Institution Names Based on Deep Learning

Academic evaluation is a process of assessing and measuring researchers, institutions, or disciplinary fields. Its goal is to evaluate their contributions and impact in the academic community, as well as to determine their reputation and status within specific disciplinary domains. Web of Science (WOS), being the most renowned global academic citation database, provides crucial data for academic evaluation. However, due to factors such as institutional changes, translation discrepancies, transcription errors in databases, and authors’ individual writing habits, there exist ambiguities in the institution names recorded in the WOS literature, which in turn affect the scientific evaluation of researchers and institutions. To address the issue of data reliability in academic evaluation, this paper proposes a WOS institution name synonym recognition framework that integrates multi-granular embeddings and multi-contextual information

Renewable prediction-driven service offloading for IoT-enabled energy systems with edge computing

The emerging of the Internet of Things (IoT) enables the interconnection among everything. With edge computing serving low-latency services, IoT makes intelligent energy management become a possibility, thereby enhancing the energy sustainability for energy systems. Currently, renewable energy is widely applied in energy systems to alleviate the carbon footprint. However, the instability and discontinuity of renewable generation decrease the quality of service (QoS) of edge servers. To address the challenge, a renewable prediction-driven service offloading method, named ReSome, is proposed. Technically, a deep-learning-based approach is designed for renewable energy prediction firstly. Next, the service offloading process is abstracted to a Markov decision process (MDP). With the predicted renewable energy amount, asynchronous advantage actor-critic (A3C) is leveraged to determine the optimal service offloading strategy. Finally, by utilizing a real-world solar power generation dataset, the experimental evaluation validates the capability and effectiveness of ReSome

Identification and Expression Analysis of R2R3-MYB Family Genes Associated with Salt Tolerance in <i>Cyclocarya paliurus</i>

R2R3-MYB transcription factors are most abundant in the MYB superfamily, while the R2R3-MYB genes play an important role in plant growth and development, especially in response to environmental stress. Cyclocarya paliurus is a multifunction tree species, and the existing resources cannot meet the requirement for its leaf production and medical use. Therefore, lands with some environmental stresses would be potential sites for developing C. paliurus plantations. However, the function of R2R3-MYB genes in C.paliurus in response to environmental stress remains unknown. In this study, to identify the roles of R2R3-MYB genes associated with salt stress response, 153 CpaMYB genes and their corresponding protein sequences were identified from the full-length transcriptome. Based on the comparison with MYB protein sequences of Arabidopsis thaliana, 69 R2R3-MYB proteins in C. paliurus were extracted for further screening combined with conserved functional domains. Furthermore, the MYB family members were analyzed from the aspects of protein sequences alignment, evolution, motif prediction, promoter cis-acting element analysis, and gene differential expression under different salt treatments using both a pot experiment and hydroponic experiment. The results showed that the R2R3-MYB genes of C.paliurus conserved functional domains, whereas four R2R3-MYB genes that might respond to salt stress via regulating plant hormone signals were identified in this study. This work provides a basis for further functional characterization of R2R3-MYB TFs in C. paliurus

Metabolome and Transcriptome Analyses Unravel the Molecular Regulatory Mechanisms Involved in Photosynthesis of Cyclocarya paliurus under Salt Stress

Photosynthesis is the primary life process in nature, and how to improve photosynthetic capacity under abiotic stresses is crucial to carbon fixation and plant productivity. As a multi-functional tree species, the leaves of Cyclocarya paliurus possess antihypertensive and hypoglycemic activities. However, the regulatory mechanism involved in the photosynthetic process of C. paliurus exposed to salinity has not yet been elucidated. In this study, the photosynthetic characteristics of C. paliurus seedlings, such as photosynthetic rate (Pn), stomatal conductance (Gs), and electron transfer rate (ETR), were investigated under different salt concentrations, while the metabolome and transcriptome analyses were conducted to unravel its molecular regulatory mechanisms. Salt stress not only significantly affected photosynthetic characteristics of C. paliurus seedlings, but also severely modified the abundance of metabolites (such as fumaric acid, sedoheptulose-7-phosphate, d-fructose-1,6-bisphosphate, and 3-phospho-d-glyceroyl phosphate) involved in central carbon metabolism, and the expression of photosynthetic genes. Through the co-expression network analysis, a total of 27 transcription factors (including ERFs, IDD, DOF, MYB, RAP) were identified to regulate photosynthetic genes under salt stress. Our findings preliminarily clarify the molecular regulatory network involved in the photosynthetic process of C. paliurus under salt stress and would drive progress in improving the photosynthetic capacity and productivity of C. paliurus by molecular technology

Weakly-Supervised Semantic Segmentation for Histopathology Images Based on Dataset Synthesis and Feature Consistency Constraint

Tissue segmentation is a critical task in computational pathology due to its desirable ability to indicate the prognosis of cancer patients. Currently, numerous studies attempt to use image-level labels to achieve pixel-level segmentation to reduce the need for fine annotations. However, most of these methods are based on class activation map, which suffers from inaccurate segmentation boundaries. To address this problem, we propose a novel weakly-supervised tissue segmentation framework named PistoSeg, which is implemented under a fully-supervised manner by transferring tissue category labels to pixel-level masks. Firstly, a dataset synthesis method is proposed based on Mosaic transformation to generate synthesized images with pixel-level masks. Next, considering the difference between synthesized and real images, this paper devises an attention-based feature consistency, which directs the training process of a proposed pseudo-mask refining module. Finally, the refined pseudo-masks are used to train a precise segmentation model for testing. Experiments based on WSSS4LUAD and BCSS-WSSS validate that PistoSeg outperforms the state-of-the-art methods. The code is released at https://github.com/Vison307/PistoSeg

Dietary DHA Enhanced the Textural Firmness of Common Carp (<i>Cyprinus carpio</i> L.) Fed Plant-Derived Diets through Restraining FoxO1 Pathways

Omega-3 fatty acids have a positive effect on the muscle textural firmness of fish, while the intrinsic mechanism is poorly understood. To investigate the potential mechanism of textural modification caused by dietary docosahexaenoic acid ( DHA) in common carp (Cyprinus carpio L.), three plant-derived diets with varying DHA levels (0%, 0.5%, 1%, D1–D3) were prepared to feed juveniles (initial weight 15.27 ± 0.77 g) for 8 weeks, and the muscular texture, fibers density, and transcriptome were analyzed. The results showed that the growth performance, muscular DHA content, fibers density, and texture of the fish fed diets D2 and D3 were significantly ameliorated compared with the fish fed diet D1. The muscular transcriptome profiles indicated that the up-regulated genes of fish fed dietary DHA mainly in response to muscle proliferation, as well as the FoxO pathway, were significantly enriched in the D2 and D3 groups. Consistent with this, the Quantitative Real-Time PCR (qRT-PCR ) assays indicated that the expression of myogenic regulatory factors (myog, myod, mrf4, mrf5) was up-regulated in the high-DHA groups. Additionally, the expression of foxo1 (inhibitor of myofiber development) mRNA was down-regulated, while its negative regulatory pathway (MAPK and PI3K) was activated in the D2 and D3 groups. The results suggested that the DHA supplementation is beneficial to modifying the muscular textural firmness of common carp fed plant-derived diets, which could be attributed to the inhibition of FoxO1 pathways