Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes (a) 34 861 poor binders and 12 683 non-binders of 1308 targets; (b) 534 prodrug-drug pairs for 121 targets; (c) 1127 co-targets of 672 targets regulated by 642 approved and 624 clinical trial drugs; (d) the collective structure-activity landscapes of 427 262 active agents of 1565 targets; (e) the profiles of drug-like properties of 33 598 agents of 1102 targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, 159 and 1658 newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. The database is accessible without login requirement at: https://idrblab.org/ttd/.

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Background: The aims of this study were to (1) evaluate the efficacy and safety of targeted antibiotics for the treatment of culture-negative prosthetic joint infection based on metagenomic next-generation sequencing results and (2) verify the accuracy and reliability of metagenomic next-generation sequencing for identifying pathogens related to culture-negative prosthetic joint infection. Methods: Ninety-seven consecutive PJI patients, including 27 patients with culture-negative prosthetic joint infection, were treated surgically at our center. Thirteen of the 27 culture-negative prosthetic joint infection patients, who were admitted before June 2017 and treated with empirical antibiotics, comprised the empirical antibiotic group (EA group), and the other 14 patients, who were admitted after June 2017 and treated with targeted antibiotics according to their metagenomic next-generation sequencing results, were classified as the targeted antibiotic group (TA group). The short-term infection control rate, incidence of antibiotic-related complications and costs were compared between the two groups. Results: Two of the patients in the EA group experienced debridement and prolonged antimicrobial therapy due to wound infection after the initial revision surgery. No recurrent infections were observed in the TA group; however, no significant difference in the infection control rate was found between the two groups (83.33% vs 100%, P = 0.217). More cases of antibiotic-related complications were recorded in the EA group (6 cases) than in the TA group (1 case), but the difference was not statistically significant (P = 0.0697). The cost of antibiotics obtained for the EA group was 20,168.37 Yuan (3236.38-45,297.16), which was higher than that found for the TA group (10, 164.16 Yuan, 2959.54-16,661.04, P = 0.04). Conclusions: Targeted antibiotic treatment for culture-negative prosthetic joint infection based on metagenomic next-generation sequencing results is associated with a favorable outcome, and metagenomic next-generation sequencing is a reliable tool for identifying pathogens related to culture-negative prosthetic joint infection.

Stomatal density (SD) and stomatal complex area (SCA) are important traits that regulate gas exchange and abiotic stress response in plants. Despite sorghum (Sorghum bicolor) adaptation to arid conditions, the genetic potential of stomata-related traits remains unexplored due to challenges in available phenotyping methods. Hence, identifying loci that control stomatal traits is fundamental to designing strategies to breed sorghum with optimized stomatal regulation. We implemented both classical and deep-learning methods to characterize genetic diversity in 311 grain sorghum accessions for stomatal traits at two different field environments. Nearly 12,000 images collected from abaxial and adaxial leaf surfaces revealed substantial variation in stomatal traits. Our study demonstrated significant accuracy between manual and deep-learning methods in predicting SD and SCA. In sorghum, SD was 32-39% greater on the abaxial vs. the adaxial surface, while SCA on the abaxial surface was 2-5% lower than on the adaxial surface. GWAS identified 71 genetic loci (38 were environment-specific) with significant genotype to phenotype associations for stomatal traits. Putative causal genes underlying the phenotypic variation were identified. Accessions with similar SCA but carrying contrasting haplotypes for SD were tested for stomatal conductance and carbon assimilation under field conditions. Our findings provide a foundation for further studies on the genetic and molecular mechanisms controlling stomata patterning and regulation in sorghum. An integrated physiological, deep learning, and genomic approach allowed us to clarify the genetic control of natural variation in stomata traits in sorghum, and can be applied to other plants.

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Aboveground plant efficiency has improved significantly in 7 recent years, and the improvement has led to a steady increase in global food 8 production. The improvement of belowground plant efficiency has potential to 9 further increase food production. However, belowground plant roots are harder 10 to study, due to inherent challenges presented by root phenotyping. Several 11 tools for identifying root anatomical features in root cross-section images 12 have been proposed. However, the existing tools are not fully automated and 13 require significant human effort to produce accurate results. To address this 14 limitation, we use a fully automated approach, specifically, the Faster Region-15 based Convolutional Neural Network (Faster R-CNN), to identify anatomical 16 traits in root cross-section images. By training Faster R-CNN models on root cross-section images, we can detect objects such as root, stele and late 18 metaxylem, and predict rectangular bounding boxes around such objects. 19 Subsequently, the bounding boxes can be used to estimate the root diameter, 20 stele diameter, late metaxylem number, and average diameter. Experimental 21 evaluation using standard object detection metrics, such as intersection-over-22 union and mean average precision, has shown that the Faster R-CNN models 23 trained on rice root cross-section images can accurately detect root, stele 24 and late metaxylem objects. Furthermore, the results have shown that the 25 measurements estimated based on predicted bounding boxes have small root 26 mean square error when compared with the corresponding ground truth values, 27 suggesting that Faster R-CNN can be used to accurately detect anatomical 28 features. A webserver for performing root anatomy using the Faster R-CNN 29 models trained on rice images is available at https://rootanatomy.org, together 30 with a link to a GitHub repository that contains a copy of the Faster R-CNN 31 code. The labeled images used for training and evaluating the Faster R-CNN 32 models are also available from the GitHub repository. 33 The crop scientific community has made significant strides in increasing 37 global food production through advances in genetics and management, with 38 majority of the progress achieved by improving aboveground plant efficiency 39 [1, 2, 3]. The belowground plant roots, which provide water and nutrients 40 for plant growth, are relatively less investigated. This is primarily because of 41 the difficulty in accessing the roots, and the complexity of phenotyping root 42 biology and function [4, 5]. Hence, root potential has largely been untapped 43 in crop improvement programs [4, 5]. Over the past decade, different root 44 phenotyping approaches have been developed for studying root architecture, 45 including basket method for root angle [6], rhizotron method for tracking root 46 branching, architecture and growth dynamics [7], shovelomics, a.k.a., root 47 crown phenotyping [8], among others. Recent advances in magnetic resonance 48imaging and X-ray computed tomogra...

In this paper, a passive/active hybrid vibration isolator is proposed to isolate micro-vibration. The passive element of the isolator is a spring-damper constructed with oil-filled corrugated pipes and the active one is an inertial actuator. A numerical model of the isolator is established through theoretical modeling of the stiffness and damping of the spring-damper and the subsystem synthesis method. On the basis of this model, frequency response functions of the disturbance and control channels are computed to describe the characteristics of the isolator. An adaptive control method based on the least mean squares algorithm is adopted to suppress transmission of micro-vibration caused by tonal, chirp or random disturbances. To verify the modeling as well as the isolation performance, tests and experiments are carried out and the results show that the computation of stiffness is effective and the passive element of the isolator has a reasonable amplification factor and a decreasing slope of -40 dB/decade. Furthermore, the active element is able to achieve remarkable attenuation of random and tonal disturbances.

Case: Two patients presented with pathological lytic bone lesions in the rib and associated soft tissue mass believed initially to represent soft tissue neoplasm. However, further consideration of infectious etiologies led to the identification of cryptococcal osteomyelitis. In one case, the microbiological culture was negative, but Cryptococcus neoformans was identified with the help of the metagenomic next-generation sequencing (mNGS) technique. Both patients received oral fluconazole-only treatment, and the infections were successfully eradicated. Conclusions: The mNGS technique helps identify cryptococcal infection in the rib.