Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300

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An imaging spectrometer with a 256 element InGaAs diode array was combined with a high throughput optical arrangement for recording high quality NIR spectra (824 nm to 1700 nm) of plastics from a distance of 25 cm within 6.3 milliseconds. The considered spectral region was assessed to be suitable for plastic identification.

Due to their pluripotency and their self-renewal capacity, human pluripotent stem cells (hPSC) provide fascinating perspectives for biomedical applications. In the long term, hPSC-derived tissue-specific cells will constitute an important source for cell replacement therapies in non-regenerative organs. These therapeutic approaches, however, will critically depend on the purity of the in vitro differentiated cell populations. In particular, remaining undifferentiated hPSC in a transplant can induce teratoma formation. In order to address this challenge, we have developed a laser-based method for the ablation of hPSC from differentiating cell cultures. Specific antibodies were directed against the hPSC surface markers tumor related antigen (Tra)-1-60 and Tra-1-81. These antibodies, in turn, were targeted with nanogold particles. Subsequent laser exposure resulted in a 98,9 +/- 0,9% elimination of hPSCs within undifferentiated cell cultures. In order to study potential side effects of laser ablation on cells negative for Tra-1-60 and Tra-1-81, hPSC were mixed with GFP-positive hPSC-derived neural precursors (hESCNP) prior to ablation. These studies showed efficient elimination of hPSC while co-treated hESCNP maintained their normal proliferation and differentiation potential. In vivo transplantation of treated and untreated mixed hPSC/hESCNP cultures revealed that laser ablation can dramatically reduce the risk of teratoma formation. Laser-assisted photothermolysis thus represents a novel contact-free method for the efficient elimination of hPSC from in vitro differentiated hPSC-derived somatic cell populations.

An Adaptive Resonance Theory Based Artificial Neural Network (ART-2a) has been compared with Multilayer Feedforward Backpropagation of Error Neural Networks (MLF-BP) and with the SIMCA classifier. All three classifiers were applied to achieve rapid sorting of post-consumer plastics by remote near-infrared (NIR) spectroscopy. A new semiconductor diode array detector based on InGaAs technology has been experimentally tested for measuring the NIR spectra. It has been found by a cross validation scheme that MLF-BP networks show a slightly better discrimination power than ART-2a networks. Both types of artificial neural networks perform significantly better than the SIMCA method. A median sorting purity of better than 98% can be guaranteed for non-black plastics. More than 75 samples per second can be identified by the combination InGaAs diode array/neural network. However, MLF-BP neural networks can definitely not extrapolate. Uninterpretable predictions were observed in case of test samples that truly belong to a particular class but that are located outside the subspace defined by training set.