Sustainable Irrigation Management of Ornamental Cordyline Fruticosa “Red Edge” Plants with Saline Water

The aim of this work was to analyze the influence of the salinity of the nutrient solution on the transpiration and growth of Cordyline fruticosa var. “Red Edge” plants. A specific irrigation management model was calibrated with the experimental data. An experiment was performed with four treatments. These treatments consisted of the application of four nutrient solutions with different electrical conductivity (ECw) levels ranging from 1.5 dS m−1 (control treatment) to 4.5 dS m−1. The results showed that day-time transpiration decreases when salt concentration in the nutrient solution increases. The transpiration of the plant in the control treatment was modelled by applying a combination method while the effect of the salinity of the nutrient solution was modelled by deriving a saline stress coefficient from the experimental data. The results showed that significant reductions in plant transpiration were observed for increasing values of ECw. The crop development and yield were also affected by the increasing salinity of the nutrient solution. A relationship between the ECw and the relative crop yield was derived

Mid-infrared photodetectors operating over an extended wavelength range up to 90 K

We report a wavelength threshold extension, from the designed value of 3.1 to 8.9 μm, in a -type heterostructure photodetector. This is associated with the use of a graded barrier and barrier offset, and arises from hole–hole interactions in the detector absorber. Experiments show that using long-pass filters to tune the energies of incident photons gives rise to changes in the intensity of the response. This demonstrates an alternative approach to achieving tuning of the photodetector response without the need to adjust the characteristic energy that is determined by the band structure

A Framework for Realistic and Systematic Multicast Performance Evaluation

Previous multicast research often makes commonly accepted but unverifed assumptions on network topologies and group member distribution in simulation studies. In this paper, we propose a framework to systematically evaluate multicast performance for different protocols. We identify a series of metrics, and carry out extensive simulation studies on these metrics with different topological models and group member distributions for three case studies. Our simulation results indicate that realistic topology and group membership models are crucial to accurate multicast performance evaluation. These results can provide guidance for multicast researchers to perform realistic simulations, and facilitate the design and development of multicast protocols

Drug Repositioning Based on Bounded Nuclear Norm Regularization

Motivation: Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug–disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug–disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug–disease associations are highly correlated. In other words, the drug–disease matrix to be completed is low-rank. Accordingly, matrix completion algorithms efficiently constructing low-rank drug–disease matrix approximations consistent with known associations can be of immense help in discovering the novel drug–disease associations. Results: In this article, we propose to use a bounded nuclear norm regularization (BNNR) method to complete the drug–disease matrix under the low-rank assumption. Instead of strictly fitting the known elements, BNNR is designed to tolerate the noisy drug–drug and disease–disease similarities by incorporating a regularization term to balance the approximation error and the rank properties. Moreover, additional constraints are incorporated into BNNR to ensure that all predicted matrix entry values are within the specific interval. BNNR is carried out on an adjacency matrix of a heterogeneous drug–disease network, which integrates the drug–drug, drug–disease and disease–disease networks. It not only makes full use of available drugs, diseases and their association information, but also is capable of dealing with cold start naturally. Our computational results show that BNNR yields higher drug–disease association prediction accuracy than the current state-of-the-art methods. The most significant gain is in prediction precision measured as the fraction of the positive predictions that are truly positive, which is particularly useful in drug design practice. Cases studies also confirm the accuracy and reliability of BNNR. Availability and implementation: The code of BNNR is freely available at https://github.com/BioinformaticsCSU/BNNR. Supplementary information Supplementary data are available at Bioinformatics online

Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study

The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images. However, it remains unknown whether this capability can also apply to the medical image domain. This paper thoroughly studies the knowledge transferability of pre-trained VLMs to the medical domain, where we show that well-designed medical prompts are the key to elicit knowledge from pre-trained VLMs. We demonstrate that by prompting with expressive attributes that are shared between domains, the VLM can carry the knowledge across domains and improve its generalization. This mechanism empowers VLMs to recognize novel objects with fewer or without image samples. Furthermore, to avoid the laborious manual designing process, we develop three approaches for automatic generation of medical prompts, which can inject expert-level medical knowledge and image-specific information into the prompts for fine-grained grounding. We conduct extensive experiments on thirteen different medical datasets across various modalities, showing that our well-designed prompts greatly improve the zero-shot performance compared to the default prompts, and our fine-tuned models surpass the supervised models by a significant margin.Comment: 14 pages, 4 figures