A Non-Invasive Follicular Thyroid Cancer Risk Prediction System Based on Deep Hybrid Multi-feature Fusion Network

Objective A non-invasive assessment of the risk of benign and malignant follicular thyroid cancer is invaluable in the choice of treatment options. The extraction and fusion of multidimensional features from ultrasound images of follicular thyroid cancer is decisive in improving the accuracy of identifying benign and malignant thyroid cancer. This paper presents a non-invasive preoperative benign and malignant risk assessment system for follicular thyroid cancer, based on the proposed deep feature extraction and fusion of ultrasound images of follicular thyroid cancer. Methods First, this study uses a convolution neural network (CNN) to obtain a global feature map of the image, and the fusion of global features cropped to local features to identify tumor images. Secondly, this tumour image is also extracted by googleNet and ResNet respectively to extract features and recognize the image. Finally, we employ an averaging algorithm to obtain the final recognition results.Results The experimental results show that the method proposed in this study achieved 89.95% accuracy, 88.46% sensitivity, 91.30% specificity and an AUC value of 96.69% in the local dataset obtained from Peking University Shenzhen Hospital, all of which are far superior to other models.Conclusion In this study, a non-invasive risk prediction system is proposed for ultrasound images of thyroid follicular tumours. We solve the problem of unbalanced sample distribution by means of an image enhancement algorithm. In order to obtain enough features to differentiate ultrasound images, a three-branched feature extraction network was designed in this study, and a balance of sensitivity and specificity is ensured by an averaging algorithm

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling

We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music. AnyGPT can be trained stably without any alterations to the current large language model (LLM) architecture or training paradigms. Instead, it relies exclusively on data-level preprocessing, facilitating the seamless integration of new modalities into LLMs, akin to the incorporation of new languages. We build a multimodal text-centric dataset for multimodal alignment pre-training. Utilizing generative models, we synthesize the first large-scale any-to-any multimodal instruction dataset. It consists of 108k samples of multi-turn conversations that intricately interweave various modalities, thus equipping the model to handle arbitrary combinations of multimodal inputs and outputs. Experimental results demonstrate that AnyGPT is capable of facilitating any-to-any multimodal conversation while achieving performance comparable to specialized models across all modalities, proving that discrete representations can effectively and conveniently unify multiple modalities within a language model. Demos are shown in https://junzhan2000.github.io/AnyGPT.github.io/Comment: 28 pages, 16 figures, under review, work in progres

A novel defined pyroptosis-related gene signature predicts prognosis and correlates with the tumour immune microenvironment in lung adenocarcinoma

Abstract Lung adenocarcinoma (LUAD) is one of the most common causes of cancer-related death. The role of pyroptosis in LUAD remains unclear. Our study aimed to identify a prognostic signature of pyroptosis-related genes (PRGs) and explore the connection of PRGs with the tumour microenvironment in LUAD. Gene expression and clinical information were obtained from The Cancer Genome Atlas database. Consensus clustering was applied to classify LUAD patients. The least absolute shrinkage and selection operator Cox and multivariate Cox regression models were used to generate a PRG-related prognostic signature. The correlations between PRGs and tumour-infiltrating immune cells or the tumour mutational burden were analysed by Spearman’s correlation analysis. In this study, 44 PRGs significantly differed in expression between LUAD and normal tissues. Based on these genes, patients were clustered into three clusters with significantly different distributions of tumour-infiltrating immune cells and immune checkpoint regulators. A total of four PRGs (NLRP1, HMGB1, CYCS, and BAK1) were used to construct a prognostic model. Significant correlations were observed between these prognostic PRGs and immune cell infiltration or the tumour mutational burden. Predictive nomogram results showed that BAK1 could be an independent prognostic biomarker in LUAD. Additionally, the expression level of BAK1 was validated in two independent Gene Expression Omnibus cohorts. Our identified prognostic PRG signature may provide insight for future studies targeting pyroptosis and the tumour microenvironment in LUAD. Future studies are needed to verify our current findings

Gramineae-legumes mixed planting effectively reduces soil and nutrient loss in orchards

Soil, water, and nutrients depletion may affect sustainable agriculture in some resource-poor areas. Implementing cover crops as a conservation management strategy mitigates the loss of water, soil, nitrogen (N), and phosphorus (P) by introducing vegetation during non-crop seasons or in the spaces between rows instead of leaving the land bare. This study aimed to compare water, soil, various carbon (C) forms, N, and P losses through leaching and surface runoff in orchard fields that were managed with either no-cover crop (NC) or cover crops, including natural grass (NG), Legume grass (LG), Gramineae grass (GG), and a mixture of Legume and Gramineae grass (MG). The findings indicate that cover crop fields exhibited a significant reduction in the runoff by 33–60 %, leaching amount by 33–51 %, soil loss by 30–53 %, and total C, N, and P by 30–48 %, 30–49 %, and 30–38 %, respectively compared to NC fields. Additionally, implementing artificial grass, particularly MG, demonstrated more significant efficacy in mitigating water and soil losses and associated N and P losses. Specifically, MG fields exhibited a 40 % and 18 % reduction in runoff and leaching as well as a reduction in total C, N, and P loss by 7 %, 12 %, and 7 %, respectively, compared to NG fields. The LG field experienced a 50 % more significant N loss than the MG field, whereas the GG runoff exhibited an increase of more than 70 %. Implementing MG coverage has significantly reduced soil erosion and consequent nutrient loss, establishing it as a viable and uncomplicated approach to conserving soil and water in orchards

Meta analysis of QTLs for fiber length, fiber micronaire and fiber strength.

<p>a: concensus map; b: meta result of fiber length; c: meta result of fiber micronaire; d: meta result of fiber strength; e: meta result of all the three traits.</p

The information of the SSR markers and SNP markers on the linkage map.

<p>The information of the SSR markers and SNP markers on the linkage map.</p

Phenotypic differences of fibers among the parents and the typical lines in the RIL population.

<p>a: Phenotypes of fibers of 0–153 and sGK9708; b: Phenotypes of fibers of two typical lines (69307 and 69362). The scale bar indicates 1 cm.</p

QTL mapping of fiber length, fiber micronaire and fiber strength.

<p>A zigzag line indicates a QTL and the top point of the zigzag line indicates the QTL position. The dotted line indicates the threshold of a QTL. The box-and whisker diagram indicates the range of a QTL.</p