In IoT-based water quality monitoring, traditional three-dimensional (3D) fluorescence spectroscopy is difficult to deploy directly on terminal sensors due to its large size, high cost, and complex structure, despite its rich information being highly advantageous for water pollution detection. In this work, a novel approach is proposed through designing a compact sensor integrated with a super-resolution reconstruction algorithm to meet the requirement of entire 3D fluorescence spectra in cloud deployment of monitoring network at minimal cost. In the design of the terminal sensor, multiple LEDs with different wavelengths serve as excitation light source and multiple filters are used to switch emission wavelengths, enabling the acquisition of a sparse 3D fluorescence spectrum. In the reconstruction algorithm, a point spread function (PSF) is employed as a constraint module to simulate the degradation process of spectra from high to low resolution by modeling the spectral characteristics of the optical components responsible for degradation, including LEDs and optical filters, which can enhance the authenticity of the reconstructed spectra. Furthermore, a joint training strategy is introduced for better robustness by jointly optimizing the parameters of PSF and deep learning network. Experiment results demonstrate that the proposed method achieves the highest reconstruction quality (PSNR: 47.57 dB, SSIM: 0.9903) and pollutant identification accuracy (98.1%) compared with seven other reconstruction methods. The proposed approach can obtain high-resolution and entire 3D fluorescence spectra for water pollution detection with low manufacturing cost and minimal data, thereby meeting the practical application requirements in the field of IoT-based water quality monitoring.

Each patient's cancer consists of multiple cell subpopulations that are inherently heterogeneous and may develop differing phenotypes such as drug sensitivity or resistance. A personalized treatment regimen should therefore target multiple oncoproteins in the cancer cell populations that are driving the treatment resistance or disease progression in a given patient to provide maximal therapeutic effect, while avoiding severe co-inhibition of non-malignant cells that would lead to toxic side effects. To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, we have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, we show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.

Understanding the quality of insight has become increasingly important with the trend of allowing users to post comments during visual exploration, yet approaches for qualifying insight are rare. This article presents a case study to investigate the possibility of characterizing the quality of insight via the interactions performed. To do this, we devised the interaction of a visualization tool-MediSyn-for insight generation. MediSyn supports five types of interactions: selecting, connecting, elaborating, exploring, and sharing. We evaluated MediSyn with 14 participants by allowing them to freely explore the data and generate insights. We then extracted seven interaction patterns from their interaction logs and correlated the patterns to four aspects of insight quality. The results show the possibility of qualifying insights via interactions. Among other findings, exploration actions can lead to unexpected insights; the drill-down pattern tends to increase the domain values of insights. A qualitative analysis shows that using domain knowledge to guide exploration can positively affect the domain value of derived insights. We discuss the study's implications, lessons learned, and future research opportunities.

Bioinformatics (2017), 33, 2017, 2413–2415, doi: 10.1093/bioinformatics/btx162 The following funding source was inadvertently omitted from the above article: European Research Council (ERC) starting grant DrugComb (Informatics approaches for the rationale selection of personaliszed cancer drug combinations) [No. 716063] This has now been added.

SUMMARY: Rational design of drug combinations has become a promising strategy to tackle the drug sensitivity and resistance problem in cancer treatment. To systematically evaluate the pre-clinical significance of pairwise drug combinations, functional screening assays that probe combination effects in a dose-response matrix assay are commonly used. To facilitate the analysis of such drug combination experiments, we implemented a web application that uses key functions of R-package SynergyFinder, and provides not only the flexibility of using multiple synergy scoring models, but also a user-friendly interface for visualizing the drug combination landscapes in an interactive manner. AVAILABILITY AND IMPLEMENTATION: The SynergyFinder web application is freely accessible at https://synergyfinder.fimm.fi ; The R-package and its source-code are freely available at http://bioconductor.org/packages/release/bioc/html/synergyfinder.html . CONTACT: [email protected].

UNLABELLED: Network pharmacology-based prediction of multi-targeted drug combinations is becoming a promising strategy to improve anticancer efficacy and safety. We developed a logic-based network algorithm, called Target Inhibition Interaction using Maximization and Minimization Averaging (TIMMA), which predicts the effects of drug combinations based on their binary drug-target interactions and single-drug sensitivity profiles in a given cancer sample. Here, we report the R implementation of the algorithm (TIMMA-R), which is much faster than the original MATLAB code. The major extensions include modeling of multiclass drug-target profiles and network visualization. We also show that the TIMMA-R predictions are robust to the intrinsic noise in the experimental data, thus making it a promising high-throughput tool to prioritize drug combinations in various cancer types for follow-up experimentation or clinical applications. AVAILABILITY AND IMPLEMENTATION: TIMMA-R source code is freely available at http://cran.r-project.org/web/packages/timma/.