Expanding the DNA-encoded library toolbox: identifying small molecules targeting RNA.

DNA-encoded library (DEL) technology is a powerful tool for small molecule identification in drug discovery, yet the reported DEL selection strategies were applied primarily on protein targets in either purified form or in cellular context. To expand the application of this technology, we employed DEL selection on an RNA target HIV-1 TAR (trans-acting responsive region), but found that the majority of signals were resulted from false positive DNA-RNA binding. We thus developed an optimized selection strategy utilizing RNA patches and competitive elution to minimize unwanted DNA binding, followed by k-mer analysis and motif search to differentiate false positive signal. This optimized strategy resulted in a very clean background in a DEL selection against Escherichia coli FMN Riboswitch, and the enriched compounds were determined with double digit nanomolar binding affinity, as well as similar potency in functional FMN competition assay. These results demonstrated the feasibility of small molecule identification against RNA targets using DEL selection. The developed experimental and computational strategy provided a promising opportunity for RNA ligand screening and expanded the application of DEL selection to a much wider context in drug discovery.

Phenotypic detection of flax plants based on improved Flax-YOLOv5.

Accurate detection and counting of flax plant organs are crucial for obtaining phenotypic data and are the cornerstone of flax variety selection and management strategies. In this study, a Flax-YOLOv5 model is proposed for obtaining flax plant phenotypic data. Based on the solid foundation of the original YOLOv5x feature extraction network, the network structure was extended to include the BiFormer module, which seamlessly integrates bi-directional encoders and converters, enabling it to focus on key features in an adaptive query manner. As a result, this improves the computational performance and efficiency of the model. In addition, we introduced the SIoU function to compute the regression loss, which effectively solves the problem of mismatch between predicted and actual frames. The flax plants grown in Lanzhou were collected to produce the training, validation, and test sets, and the detection results on the validation set showed that the average accuracy (mAP@0.5) was 99.29%. In the test set, the correlation coefficients (R) of the model's prediction results with the manually measured number of flax fruits, plant height, main stem length, and number of main stem divisions were 99.59%, 99.53%, 99.05%, and 92.82%, respectively. This study provides a stable and reliable method for the detection and quantification of flax phenotypic characteristics. It opens up a new technical way of selecting and breeding good varieties.

Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging.

Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed's nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed species widely cultivated in Northwest China as the subjects of our study. Firstly, we gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan, and utilized the SPXY algorithm to classify the sample set. Subsequently, the spectral data underwent seven distinct preprocessing methods, revealing that the PLSR model exhibited superior performance after being processed with the SG smoothing method. Feature wavelength extraction was carried out using the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS). Finally, four quantitative analysis models, namely Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Principal Component Regression (PCR), were individually established. Experimental results demonstrated that among all the models for predicting protein content, the SG-CARS-MLR model predicted the best, with and of 0.9563 and 0.9336, with the corresponding Root Mean Square Error Correction (RMSEC) and Root Mean Square Error Prediction (RMSEP) of 0.4892 and 0.5616, respectively. In the optimal prediction models for oil content, linoleic acid and lignan, the Rp2 was 0.8565, 0.8028, 0.9343, and the RMSEP was 0.8682, 0.5404, 0.5384, respectively. The study results show that hyperspectral imaging technology has excellent potential for application in the detection of quality characteristics of flaxseed and provides a new option for the future non-destructive testing of the nutritional quality of flaxseed.

NL2Color: Refining Color Palettes for Charts with Natural Language.

Choice of color is critical to creating effective charts with an engaging, enjoyable, and informative reading experience. However, designing a good color palette for a chart is a challenging task for novice users who lack related design expertise. For example, they often find it difficult to articulate their abstract intentions and translate these intentions into effective editing actions to achieve a desired outcome. In this work, we present NL2Color, a tool that allows novice users to refine chart color palettes using natural language expressions of their desired outcomes. We first collected and categorized a dataset of 131 triplets, each consisting of an original color palette of a chart, an editing intent, and a new color palette designed by human experts according to the intent. Our tool employs a large language model (LLM) to substitute the colors in original palettes and produce new color palettes by selecting some of the triplets as few-shot prompts. To evaluate our tool, we conducted a comprehensive two-stage evaluation, including a crowd-sourcing study ( N=71) and a within-subjects user study ( N=12). The results indicate that the quality of the color palettes revised by NL2Color has no significantly large difference from those designed by human experts. The participants who used NL2Color obtained revised color palettes to their satisfaction in a shorter period and with less effort.

[Isolation, identification and characterization of 68 protease-producing bacterial strains from the Arctic].

OBJECTIVE: We screened and identified protease-producing bacterial strains from the Arctic, the results would help find cold-adapted protease. METHODS: In total 68 protease-producing strains were screened from the Arctic using the casein-agar plate under low temperature. All strains were classified using the 16S rRNA gene-restriction fragment length polymorphism (RFLP) and traditional phenotypic test. One strain was chosen to be the representative strain from each group respectively and identified using 16S rRNA gene sequence analysis, GenBank database Basic Local Alignment Search Tool (BLAST), phylogenetic analysis and phenotypic features analysis. The enzymatic properties of the representative strains were determined. RESULTS: The 68 strains belonged to 3 groups (54.41%, 42.65% and 2.94%), and strains 6, 11 and 52 were the representative strains respectively. The results of the 16S rRNA gene sequence analysis showed that: Strain 11 was most closely related to Chryseobacterium scophthalmum with 98.24% sequence similarity; strain 52 was most closely related to Stenotrophomonas rhizophila with 98.55% sequence similarity; strain 6 was most closely related to Stenotrophomonas rhizophila with 96.50% sequence similarity, which might represent a novel species of the genus. The phenotypic study showed that: strains 6, 11 and 52 were Gram negative, straight rod-shaped, did not produce extracellular lipase and amylase, possessed strong proteolytic activity. The optimal temperature and pH for the enzyme activity were 55 degrees C and 6.7 for the protease from strain 6, 40 degrees C and 8.5 for the protease from strain 11 and 65 degrees C and 7.4 for the protease from strain 52. CONCLUSION: The research firstly introduced the distribution of Stenotrophomonas and Chryseobacterium specieses in the Arctic marine water, extended the diversity of the protease-producing bacteria from the Arctic, and provided a useful basis for further study of cold-adapted protease.

Identification of wheat seedling varieties based on MssiapNet.

Introduction: In the actual planting of wheat, there are often shortages of seedlings and broken seedlings on long ridges in the field, thus affecting grain yield and indirectly causing economic losses. Variety identification of wheat seedlings using physical methods timeliness and is unsuitable for universal dissemination. Recognition of wheat seedling varieties using deep learning models has high timeliness and accuracy, but fewer researchers exist. Therefore, in this paper, a lightweight wheat seedling variety recognition model, MssiapNet, is proposed. Methods: The model is based on the MobileVit-XS and increases the model's sensitivity to subtle differences between different varieties by introducing the scSE attention mechanism in the MV2 module, so the recognition accuracy is improved. In addition, this paper proposes the IAP module to fuse the identified feature information. Subsequently, training was performed on a self-constructed real dataset, which included 29,020 photographs of wheat seedlings of 29 varieties. Results: The recognition accuracy of this model is 96.85%, which is higher than the other nine mainstream classification models. Although it is only 0.06 higher than the Resnet34 model, the number of parameters is only 1/3 of that. The number of parameters required for MssiapNet is 29.70MB, and the single image Execution time and the single image Delay time are 0.16s and 0.05s. The MssiapNet was visualized, and the heat map showed that the model was superior for wheat seedling variety identification compared with MobileVit-XS. Discussion: The proposed model has a good recognition effect on wheat seedling varieties and uses a few parameters with fast inference speed, which makes it easy to be subsequently deployed on mobile terminals for practical performance testing.

[Study on the white channel system carved on the wooden acupoint figurine of Laoguanshan: comments on lacquer-coating acupoint figure].

There are two systems as the red channel system and the white channel system carved or painted on the wooden figurine of Laoguanshan of Benque school. The two systems are horizontally staggered each other without overlapped. The red channel system, similar to Shuangbaoshan wooden figurine, have channels, but without points. For the white channel system, the running courses of channels result from the sensation distributions of the points after optional stimulation. The Laoguanshan wooden figurine focuses on the illustration of the white channel system, named as white channel figurine. Compared with the Shuangbaoshan red channel figurine, together with examples, such as the running course of the white channel related to the meridian of heart-transfer-point, the white channel related to the belt vessel linking to lung-transfer-point, stomach-transfer-point and kidney-transfer-point, as well as the corresponding photographs. It is indicated that the Laoguanshan white channel figurine is a training aid for testing the sensation marching along channel (SMC) caused by transfer-point stimulation. The white channel system is a flexible way of channel. The study aims to observe the QI/SMC reaching the affected area and contributes to clinical practice. This discovery is not related to the "intermediate link theory" in the Yellow Emperor meridian system.

[Which is right? "Theory of channel" or "Theory of blood vessels"].

The ancient medical science of Jingmai (Channel or blood vessel) applied the running sensation along channels of the so-called "Mai" to diagnose and treat diseases. Unfortunately, this had been lost in the Han Dynasty. Related information is recorded in the official history; there are related evidences in the unearthed Mai shu (The Book of Channel) and "Mairen (The Statue of Channel)", including the rediscovery and modern researches on the running sensation along channels; the successful cases of diagnosis and treatment by the method of running sensation along channels. The scholars supported the theory of blood vessel query the above ideas, which need to assemble large numbers of researches of the theory of channel to resolve the problems.