The identification of synergistic chemotherapeutic agents from a large pool of candidates is highly challenging. Here, we present a Ranking-system of Anti-Cancer Synergy (RACS) that combines features of targeting networks and transcriptomic profiles, and validate it on three types of cancer. Using data on human β-cell lymphoma from the Dialogue for Reverse Engineering Assessments and Methods consortium we show a probability concordance of 0.78 compared with 0.61 obtained with the previous best algorithm. We confirm 63.6% of our breast cancer predictions through experiment and literature, including four strong synergistic pairs. Further in vivo screening in a zebrafish MCF7 xenograft model confirms one prediction with strong synergy and low toxicity. Validation using A549 lung cancer cells shows similar results. Thus, RACS can significantly improve drug synergy prediction and markedly reduce the experimental prescreening of existing drugs for repurposing to cancer treatment, although the molecular mechanism underlying particular interactions remains unknown.

Minutiae extraction is of critical importance in automated fingerprint recognition.Previous works on rolled/slap fingerprints failed on latent fingerprints due to noisy ridge patterns and complex background noises. In this paper, we propose a new way to design deep convolutional network combining domain knowledge and the representation ability of deep learning. In terms of orientation estimation, segmentation, enhancement and minutiae extraction, several typical traditional methods performed well on rolled/slap fingerprints are transformed into convolutional manners and integrated as an unified plain network. We demonstrate that this pipeline is equivalent to a shallow network with fixed weights. The network is then expanded to enhance its representation ability and the weights are released to learn complex background variance from data, while preserving end-to-end differentiability. Experimental results on NIST SD27 latent database and FVC 2004 slap database demonstrate that the proposed algorithm outperforms the state-of-the-art minutiae extraction algorithms. Code is made publicly available at: https://github.com/felixTY/FingerNet.

We present a new Euclidean distance for images, which we call IMage Euclidean Distance (IMED). Unlike the traditional Euclidean distance, IMED takes into account the spatial relationships of pixels. Therefore, it is robust to small perturbation of images. We argue that IMED is the only intuitively reasonable Euclidean distance for images. IMED is then applied to image recognition. The key advantage of this distance measure is that it can be embedded in most image classification techniques such as SVM, LDA, and PCA. The embedding is rather efficient by involving a transformation referred to as Standardizing Transform (ST). We show that ST is a transform domain smoothing. Using the Face Recognition Technology (FERET) database and two state-of-the-art face identification algorithms, we demonstrate a consistent performance improvement of the algorithms embedded with the new metric over their original versions.

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Seeking potential toxic and side effects for clinically available drugs is considerably beneficial in pharmaceutical safety evaluation. In this article, the authors developed an integrated microfluidic array system for phenotype-based evaluation of toxic and teratogenic potentials of clinical drugs by using zebrafish ͑Danio rerio͒ embryos as organism models. The microfluidic chip consists of a concentration gradient generator from upstream and an array of open embryonic culture structures by offering continuous stimulation in gradients and providing guiding, cultivation and exposure to the embryos, respectively. The open culture reservoirs are amenable to long-term embryonic culturing. Gradient test substances were delivered in a continuous or a developmental stage-specific manner, to induce embryos to generate dynamic developmental toxicity and teratogenicity. Developmental toxicity of doxorubicin on zebrafish eggs were quantitatively assessed via heart rate, and teratological effects were characterized by pericardial impairment, tail fin, notochord, and SV-BA distance /body length. By scoring the teratogenic severity, we precisely evaluated the time-and dose-dependent damage on the chemical-exposed embryos. The simple and easily operated method presented herein demonstrates that zebrafish embryo-based pharmaceutic assessment could be performed using microfluidic systems and holds a great potential in high-throughput screening for new compounds at single animal resolution.