MOTIVATION: Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Drugs with human genetic evidence are more likely to advance successfully through clinical trials toward Food and Drug Administration approval. Single gene-based drug repositioning methods have been implemented, but approaches leveraging a broad spectrum of molecular signatures remain underexplored. RESULTS: We propose a framework called "Transcriptome-informed Reversal Distance" (TReD) that embeds the disease signatures and drug response profiles into a high-dimensional normed space to quantify the reversal potential of candidate drugs in a disease-related cell-based screening. We applied TReD to COVID-19, type 2 diabetes, and Alzheimer's disease (AD), identifying 36, 16, and 11 candidate drugs, respectively. Among these, literature supports 69% (25/36), 31% (5/16), and 64% (7/11) of the drugs, with clinical trials conducted for seven COVID-19 candidates and three AD candidates. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screening that has the potential to accelerate the search for new therapeutic strategies. AVAILABILITY AND IMPLEMENTATION: Source code and datasets considered in this study are available at Github (https://github.com/zdangm/TReD). An archived snapshot is deposited at Zenodo (https://doi.org/10.5281/zenodo.16791909).

Recent progress has shown that integrating multiscale visual features with advanced Transformer architectures is a promising approach for remote sensing image captioning (RSIC). However, the lack of local modeling ability in self-attention may potentially lead to inaccurate contextual information. Moreover, the scarcity of trainable image-caption pairs poses challenges in effectively harnessing the semantic alignment between images and texts. To mitigate these issues, we propose a Multiscale Grouping Transformer with Contrastive Language-Image Pre-training (CLIP) latents (MG-Transformer) for RSIC. First of all, a CLIP image embedding and a set of region features are extracted within a Multi-level Feature Extraction module. To achieve a comprehensive image representation, a Semantic Correlation module is designed to integrate the image embedding and region features with an attention gate. Subsequently, the integrated image features are fed into a Transformer model. The Transformer encoder utilizes dilated convolutions with different dilation rates to obtain multiscale visual features. To enhance the local modeling ability of the self-attention mechanism in the encoder, we introduce a Global Grouping Attention mechanism. This mechanism incorporates a grouping operation into self-attention, allowing each attention head to focus on different contextual information. The Transformer decoder then adopts the Meshed Cross-Attention mechanism to establish relationships between various scales of visual features and text features. This facilitates the generation of captions for images by the decoder. Experimental results on three RSIC datasets demonstrate the superiority of the proposed MG-Transformer. The code will be publicly available at https://github.com/One-paper-luck/MG-Transformer.

Unmanned aerial vehicles (UAV) with onboard sensors become a cost-effective way of crop remote sensing in largescale farms. However, current vision-based crop aerial sensing methods suffer from occlusion issue and require large amount of annotation data. In this paper, we target one particular crop species, corn, and propose to use 3D modeling and simulation to help resolve the issues. We first develop a corn-field 3D model and a crop aerial sensing (CAS) simulation framework. Then we use the CAS framework to generate synthetic data to train various deep learning models for corn leaf segmentation. In addition, we change the 3D model parameters in CAS, e.g., distances between individual corn plants, to derive leaf area index (LAI) correction coefficients for various corn plant and row spacings. Our experimental results from real-world UAV images show that our leaf segmentation model using synthetic data from the CAS framework outperforms state-of-the-art segmentation models by 1.4-3.3%. Our simulation results show that the plant and row spacings of a corn-field have significant effects on correcting the UAV image-based LAI, which can be underestimated by a factor of 2.6, due to the overlap and occlusion issues.

Most aerial object detectors mainly adopt anchor-based methods, which require manually designed rough anchors. However, these anchors usually contain background redundancy unrelated to the objects, decreasing detection performance and introducing additional calculations. In this paper, we propose an orientation-aware anchor-free network (OAF-Net) for object detection. OAF-Net first produces coarse oriented boxes by coarse location network based on pixel-level regression and then refines them with aligned features. In particular, we also apply a new metric to measure the orientation-aware center-ness, which is a weighting strategy of positive samples to reduce the different contributions of the low-quality detection boxes. Experiments on the DOTA1.0 and HRSC2016 datasets reveal that OAF-Net outperforms the state-of-the-art in terms of comprehensive detection performance for various objects with different scales and orientations in aerial images.

As climate continues to warm, detailed geospatial spectral, structural, and thermal information, related to water, carbon, and energy cycling, are required for modeling the future state of the Arctic biome. To address these needs we have developed a cost-effective, multi-sensor UAS-based remote sensing platform for acquiring high resolution spectral, structural, and thermal measurements of Arctic vegetation. We successfully deployed this remote sensing platform in three sites along an elevation gradient near Nome, Alaska in summer 2017. Corresponding workflows have been further developed for assessing spectral, structural, and thermal patterns of arctic tundra biomes from the collected datasets. Our results demonstrate that the UAS platform can successfully (1) map heterogenous vegetation composition and (2) assess corresponding spectral, structural, and thermal patterns of Arctic tundra biomes. This study not only presents a novel and innovative approach for collecting high resolution spectral, structural and thermal information for characterizing Arctic tundra biome patterns, but also a basis for informing the modeling of climate feedbacks between the biosphere and the atmosphere in response to ongoing global change.

Accurate characterization of post-fire changes in tree mortality and carbon storage is critical in understanding and simulating future forest responses to wildfires in response to climate change. LiDAR remote sensing has been successfully used to estimate aboveground biomass (AGB) in undisturbed forests, but few studies focused on effects of burn severity on post-fire tree mortality and AGB. Specifically, it is not clear how burn severity would affect forest mortality and post-fire AGB patterns in mixed forests in Eastern U.S. In this study, we examined short-term changes in tree mortality and AGB across a burn gradient in a Pine Barrens ecosystem in the Eastern United States, using field observations, LiDAR and hyperspectral data. Results show that fusion of LiDAR and hyperspectral images can characterize key forest ecosystem attributes at fine spatial and spectral resolutions, and provide consistent and accurate estimation of post-fire forest conditions at landscape scales. Both post-fire mortality and AGB are highly correlated with burn severity, and we found a linear relationship between burn severity and post-fire AGB. Our results can be used to predict post-fire mortality and AGB changes and provide quantitative evidence for informed forest fire management in similar ecosystems in the U.S.

Low‐rank approximation has shown great potential in various image tasks. It is found that there is a specific functional relationship about singular values between the original image and a series of noisy images, which can be used to construct the singular values of a noise‐free image. In this study, the authors propose a novel denoising method based on the above facts and low‐rank approximation theory. Firstly, they estimate the noise energy distribution of the group matrix in the singular value decomposition (SVD) domain using the energy characteristics of the image with different noise levels. The energy distribution of the noise is shrunk to obtain the energy distribution of the true signal. Then, based on the optimal energy compaction property of SVD, the low‐rank property of matrix is constrained in the SVD domain to obtain the low‐rank approximation of the matrix. Moreover, an iterative back projection method is adopted in this study to suppress residual noise. A new noise standard deviation estimation approach, targeted at the back projection process, is proposed to effectively optimise the denoising results during the iteration. Experimental results show that the authors’ method efficiently decreases the noise and achieves comparable denoising performance to the state‐of‐the‐art methods regarding both quantitative measurement and visual effect.

Unmodified or as a poly[lactide-co-glycolide] nanoparticle, tetraiodothyroacetic acid (tetrac) acts at the integrin αvβ3 receptor on human cancer cells to inhibit tumor cell proliferation and xenograft growth. To study in vitro the pharmacodynamics of tetrac formulations in the absence of and in conjunction with other chemotherapeutic agents, we developed a perfusion bellows cell culture system. Cells were grown on polymer flakes and exposed to various concentrations of tetrac, nano-tetrac, resveratrol, cetuximab, or a combination for up to 18 days. Cells were harvested and counted every one or two days. Both NONMEM VI and the exact Monte Carlo parametric expectation maximization algorithm in S-ADAPT were utilized for mathematical modeling. Unmodified tetrac inhibited the proliferation of cancer cells and did so with differing potency in different cell lines. The developed mechanism-based model included two effects of tetrac on different parts of the cell cycle which could be distinguished. For human breast cancer cells, modeling suggested a higher sensitivity (lower IC50) to the effect on success rate of replication than the effect on rate of growth, whereas the capacity (Imax) was larger for the effect on growth rate. Nanoparticulate tetrac (nano-tetrac), which does not enter into cells, had a higher potency and a larger anti-proliferative effect than unmodified tetrac. Fluorescence-activated cell sorting analysis of harvested cells revealed tetrac and nano-tetrac induced concentration-dependent apoptosis that was correlated with expression of pro-apoptotic proteins, such as p53, p21, PIG3 and BAD for nano-tetrac, while unmodified tetrac showed a different profile. Approximately additive anti-proliferative effects were found for the combinations of tetrac and resveratrol, tetrac and cetuximab (Erbitux), and nano-tetrac and cetuximab. Our in vitro perfusion cancer cell system together with mathematical modeling successfully described the anti-proliferative effects over time of tetrac and nano-tetrac and may be useful for dose-finding and studying the pharmacodynamics of other chemotherapeutic agents or their combinations.

As multi/many core processors become prevalent, programming language is important in constructing efficient parallel applications. In this work, we build a multithreaded video mining application with Java, examine the thread profiling information and micro-architecture metrics to identify the factors limiting the scalability, and employ a number of ways to improve performance. Besides, we conduct some thread scheduling experiments. According to the experiments and detailed analysis, we conclude that for this video mining application: (1) Java is a good parallel language candidate for many core processors in terms of performance, scalability, and ease of programming; (2) Thread affinity mechanism is effective in improving data locality, but brings little benefit to multithreaded Java application due to its conservative memory model in JVM