Analysis of Genetic Diversity in 73 Kentucky Bluegrass Materials by SSR and SRAP Markers

Kentucky bluegrass (Poa pratensisL.) (KBG) is a commonly used grass that possesses excellent quality, as well as a complex genetic background and reproductive patterns. In this study, a total of 73 KBG germplasms were collected, of which 49 were imported varieties, 5 were Chinese breeding varieties, and 19 were wild materials. A total of 70 simple sequence repeat (SSR) and 75 sequence-related amplification polymorphism (SRAP) markers were selected to use for genetic diversity analysis. From these studies, high levels of polymorphisms were observed in SRAPs (91.8%) and SSRs (94.5%), respectively. Three dendrograms that were generated from SRAP, SSR, and SRAP+SSR combined data revealed a general similarity for the positioning of the majority of materials. However, certain materials, including Z65, Z25, and Z27, were found to be located in diverse clusters among different dendrograms. Further analysis demonstrated no significant association between geographical origin and molecular marker clusters in the wild materials. Combined with the seedling phenotype identification carried out in our prior study, it seems as though there is no significant relationship between agronomic characterization and marker-based clustering in these materials, except for in the case of leaf color. These studies provided an increased understanding of genetic diversity among KBG materials, which will be beneficial for genetic improvement and germplasm conservation in the future

Lightweight cotton diseases real-time detection model for resource-constrained devices in natural environments

Cotton, a vital textile raw material, is intricately linked to people’s livelihoods. Throughout the cotton cultivation process, various diseases threaten cotton crops, significantly impacting both cotton quality and yield. Deep learning has emerged as a crucial tool for detecting these diseases. However, deep learning models with high accuracy often come with redundant parameters, making them challenging to deploy on resource-constrained devices. Existing detection models struggle to strike the right balance between accuracy and speed, limiting their utility in this context. This study introduces the CDDLite-YOLO model, an innovation based on the YOLOv8 model, designed for detecting cotton diseases in natural field conditions. The C2f-Faster module replaces the Bottleneck structure in the C2f module within the backbone network, using partial convolution. The neck network adopts Slim-neck structure by replacing the C2f module with the GSConv and VoVGSCSP modules, based on GSConv. In the head, we introduce the MPDIoU loss function, addressing limitations in existing loss functions. Additionally, we designed the PCDetect detection head, integrating the PCD module and replacing some CBS modules with PCDetect. Our experimental results demonstrate the effectiveness of the CDDLite-YOLO model, achieving a remarkable mean average precision (mAP) of 90.6%. With a mere 1.8M parameters, 3.6G FLOPS, and a rapid detection speed of 222.22 FPS, it outperforms other models, showcasing its superiority. It successfully strikes a harmonious balance between detection speed, accuracy, and model size, positioning it as a promising candidate for deployment on an embedded GPU chip without sacrificing performance. Our model serves as a pivotal technical advancement, facilitating timely cotton disease detection and providing valuable insights for the design of detection models for agricultural inspection robots and other resource-constrained agricultural devices

Adapting Segment Anything Model (SAM) through Prompt-based Learning for Enhanced Protein Identification in Cryo-EM Micrographs

Cryo-electron microscopy (cryo-EM) remains pivotal in structural biology, yet the task of protein particle picking, integral for 3D protein structure construction, is laden with manual inefficiencies. While recent AI tools such as Topaz and crYOLO are advancing the field, they do not fully address the challenges of cryo-EM images, including low contrast, complex shapes, and heterogeneous conformations. This study explored prompt-based learning to adapt the state-of-the-art image segmentation foundation model Segment Anything Model (SAM) for cryo-EM. This focus was driven by the desire to optimize model performance with a small number of labeled data without altering pre-trained parameters, aiming for a balance between adaptability and foundational knowledge retention. Through trials with three prompt-based learning strategies, namely head prompt, prefix prompt, and encoder prompt, we observed enhanced performance and reduced computational requirements compared to the fine-tuning approach. This work not only highlights the potential of prompting SAM in protein identification from cryo-EM micrographs but also suggests its broader promise in biomedical image segmentation and object detection

TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback

Learning large-scale pre-trained models on broad-ranging data and then transfer to a wide range of target tasks has become the de facto paradigm in many machine learning (ML) communities. Such big models are not only strong performers in practice but also offer a promising way to break out of the task-specific modeling restrictions, thereby enabling task-agnostic and unified ML systems. However, such a popular paradigm is mainly unexplored by the recommender systems (RS) community. A critical issue is that standard recommendation models are primarily built on categorical identity features. That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms. To pursue the transferable recommendations, we propose studying pre-trained RS models in a novel scenario where a user's interaction feedback involves a mixture-of-modality (MoM) items, e.g., text and images. We then present TransRec, a very simple modification made on the popular ID-based RS framework. TransRec learns directly from the raw features of the MoM items in an end-to-end training manner and thus enables effective transfer learning under various scenarios without relying on overlapped users or items. We empirically study the transferring ability of TransRec across four different real-world recommendation settings. Besides, we look at its effects by scaling source and target data size. Our results suggest that learning neural recommendation models from MoM feedback provides a promising way to realize universal RS

An efficient protocol of potato virus A eradication by thermotherapy coupled with in vitro culture of potato (Solanum tuberosum)

With the aim of developing an effective protocol for virus elimination from potato (Solanum tuberosum L.) plantlets, thermotherapy coupled with isolating the first nodal cuttings by in vitro culture was successful to potato virus A (PVA) elimination. The survival ratio of potato plantlets was affective by thermotherapy temperatures and durations. The optimal thermotherapy temperature was 36±1 oC with highest survival ratio and effective elimination. The results of RT-PCR indicated that the regenerated plantlets obtained from the first cycle (four weeks) of thermotherapy in daytime at 36±1 oC with light intensity 40 mmole/m/s for 12 hr, and 20±1 oC in darkness for 12 hr had PVA infected. While isolated the first nodal cuttings and followed by thermotherapy at the first cycle conditions for another two weeks, the PVA could be eliminated. Thermotherapy was given by culturing the nodal cutting from the infected of PVA for six weeks in total on MS medium, and the PVA-free plantlets were obtained. In concluded that the protocol of thermotherapy coupled with isolating the first nodal cuttings by in vitro culture in the study can be effectively used for virus free plantlets in potato, and probably also for other vegetable propagated plant species

GeodesicEmbedding (GE): a high-dimensional embedding approach for fast geodesic distance queries

In this paper, we develop a novel method for fast geodesic distance queries. The key idea is to embed the mesh into a high-dimensional space, such that the Euclidean distance in the high-dimensional space can induce the geodesic distance in the original manifold surface. However, directly solving the high-dimensional embedding problem is not feasible due to the large number of variables and the fact that the embedding problem is highly nonlinear. We overcome the challenges with two novel ideas. First, instead of taking all vertices as variables, we embed only the saddle vertices, which greatly reduces the problem complexity. We then compute a local embedding for each non-saddle vertex. Second, to reduce the large approximation error resulting from the purely Euclidean embedding, we propose a cascaded optimization approach that repeatedly introduces additional embedding coordinates with a non-Euclidean function to reduce the approximation residual. Using the precomputation data, our approach can determine the geodesic distance between any two vertices in near-constant time. Computational testing results show that our method is more desirable than previous geodesic distance queries methods

Corrigendum: Inhibition of O-GlcNAc transferase sensitizes prostate cancer cells to docetaxel

The expression of O-GlcNAc transferase (OGT) and its catalytic product, O-GlcNAcylation (O-GlcNAc), are elevated in many types of cancers, including prostate cancer (PC). Inhibition of OGT serves as a potential strategy for PC treatment alone or combinational therapy. PC is the second common cancer type in male worldwide, for which chemotherapy is still the first-line treatment. However, the function of inhibition of OGT on chemotherapeutic response in PC cells is still unknown. In this study, we show that inhibition of OGT by genetic knockdown using shRNA or by chemical inhibition using OGT inhibitors sensitize PC cells to docetaxel, which is the most common chemotherapeutic agent in PC chemotherapy. Furthermore, we identified that microRNA-140 (miR-140) directly binds to OGT mRNA 3′ untranslated region and inhibits OGT expression. Moreover, docetaxel treatment stimulates miR-140 expression, whereas represses OGT expression in PC cells. Overexpression of miR-140 enhanced the drug sensitivity of PC cells to docetaxel, which could be reversed by overexpression of OGT. Overall, this study demonstrates miR-140/OGT axis as therapeutic target in PC treatment and provides a promising adjuvant therapeutic strategy for PC therapy

When It's Heavier: Interfacial and Solvation Chemistry of Isotopes in Aqueous Electrolytes for Zn-ion Batteries

The electrochemical effect of isotope (EEI) of water is introduced in the Zn-ion batteries (ZIBs) electrolyte to deal with the challenge of severe side reactions and massive gas production. Due to the low diffusion and strong coordination of ions in D2O, the possibility of side reactions is decreased, resulting in a broader electrochemically stable potential window, less pH change, and less zinc hydroxide sulfate (ZHS) generation during cycling. Moreover, we demonstrate that D2O eliminates the different ZHS phases generated by the change of bound water during cycling because of the consistently low local ion and molecule concentration, resulting in a stable interface between the electrode and electrolyte. The full cells with D2O-based electrolyte demonstrated more stable cycling performance which displayed ∼100 % reversible efficiencies after 1,000 cycles with a wide voltage window of 0.8–2.0 V and 3,000 cycles with a normal voltage window of 0.8–1.9 V at a current density of 2 A g−1