Urban mobility planning as a frame for urban design of squares and streets, the Peja case in Kosovo

Urban streets and squares have a dual function: as links in transport networks and as places for accommodating urban life. In this paper an integrated approach of urban design of the public domain is developed, highlighting the layers multimodal mobility planning can provide for the design. The approach is shown on the basis of some cases for the city of Peja (Kosovo). The designs of two strategic elements in the urban network, the railway station boulevard and the Haxhi Zeka Square as well as a district collector street within the Zatra living area are described. These design projects were elaborated by a mixed international and local team, working together in design studios within the frame of the ‘MOBKOS’ cooperation project (www.mobkos.eu). This project was co financed by the Flemish and the Kosovar Governments in the period 2009-2011. A well the structured road categorisation system and urban parking strategy are determining elements of the design. But even so a ‘soft spine’ accommodating walking and biking routes and a new system of urban bus routes are important layers of the design. The paper doesn’t call for a traffic engineering dominated design. It is gradually understood that the prevailing ‘city for cars’ practices in Kosovo (that had already shown its limits in Pristine) would lead Peja into a dead end street. That is why concepts such as urban boulevards, ‘shared spaces’, green trails… are used in the designs. The same international team is preparing design studios in the city of Shkodra in Albania

Investigating the biological relevance in trained embedding representations of protein sequences

As genome sequencing is becoming faster and cheaper, an abundance of DNA and protein sequence data is available. However, experimental annotation of structural or functional information develops at a much slower pace. Therefore, machine learning techniques have been widely adopted to make accurate predictions on unseen sequence data. In recent years, deep learning has been gaining popularity, as it allows for effective end-to-end learning. One consideration for its application on sequence data is the choice for a suitable and effective sequence representation strategy. In this paper, we investigate the significance of three common encoding schemes on the multi-label prediction problem of Gene Ontology (GO) term annotation, namely a one-hot encoding, an ad-hoc trainable embedding, and pre-trained protein vectors, using different hyper-parameters. We found that traditional unigram one-hot encodings achieved very good results, only slightly outperformed by unigram ad-hoc trainable embeddings and bigram pre-trained embeddings (by at most 3%for the F maxscore), suggesting the exploration of different encoding strategies to be potentially beneficial. Most interestingly, when analyzing and visualizing the trained embeddings, we found that biologically relevant (dis)similarities between amino acid n-grams were implicitly learned, which were consistent with their physiochemical properties

Urban mobility planning as a frame for urban design of squares and streets, the Peja case in Kosovo

Urban streets and squares have a dual function: as links in transport networks and as places for accommodating urban life. In this paper an integrated approach of urban design of the public domain is developed, highlighting the layers multimodal mobility planning can provide for the design. The approach is shown on the basis of some cases for the city of Peja (Kosovo). The designs of two strategic elements in the urban network, the railway station boulevard and the Haxhi Zeka Square as well as a district collector street within the Zatra living area are described. These design projects were elaborated by a mixed international and local team, working together in design studios within the frame of the 'MOBKOS' cooperation project (www.mobkos.eu). This project was co financed by the Flemish and the Kosovar Governments in the period 2009-2011. A well the structured road categorisation system and urban parking strategy are determining elements of the design. But even so a 'soft spine' accommodating walking and biking routes and a new system of urban bus routes are important layers of the design. The paper doesn't call for a traffic engineering dominated design. It is gradually understood that the prevailing 'city for cars' practices in Kosovo (that had already shown its limits in Pristine) would lead Peja into a dead end street. That is why concepts such as urban boulevards, 'shared spaces', green trails... are used in the designs. The same international team is preparing design studios in the city of Shkodra in Albania

Web Applicable Computer-aided Diagnosis of Glaucoma Using Deep Learning

Glaucoma is a major eye disease, leading to vision loss in the absence of proper medical treatment. Current diagnosis of glaucoma is performed by ophthalmologists who are often analyzing several types of medical images generated by different types of medical equipment. Capturing and analyzing these medical images is labor-intensive and expensive. In this paper, we present a novel computational approach towards glaucoma diagnosis and localization, only making use of eye fundus images that are analyzed by state-of-the-art deep learning techniques. Specifically, our approach leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) for glaucoma diagnosis and localization, respectively. Quantitative and qualitative results, as obtained for a small-sized dataset with no segmentation ground truth, demonstrate that the proposed approach is promising, for instance achieving an accuracy of 0.91$\pm0.02$ and an ROC-AUC score of 0.94 for the diagnosis task. Furthermore, we present a publicly available prototype web application that integrates our predictive model, with the goal of making effective glaucoma diagnosis available to a wide audience.Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:cs/010120

Utilizing Mutations to Evaluate Interpretability of Neural Networks on Genomic Data

Even though deep neural networks (DNNs) achieve state-of-the-art results for a number of problems involving genomic data, getting DNNs to explain their decision-making process has been a major challenge due to their black-box nature. One way to get DNNs to explain their reasoning for prediction is via attribution methods which are assumed to highlight the parts of the input that contribute to the prediction the most. Given the existence of numerous attribution methods and a lack of quantitative results on the fidelity of those methods, selection of an attribution method for sequence-based tasks has been mostly done qualitatively. In this work, we take a step towards identifying the most faithful attribution method by proposing a computational approach that utilizes point mutations. Providing quantitative results on seven popular attribution methods, we find Layerwise Relevance Propagation (LRP) to be the most appropriate one for translation initiation, with LRP identifying two important biological features for translation: the integrity of Kozak sequence as well as the detrimental effects of premature stop codons.Comment: Accepted for publication at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022), Workshop on Learning Meaningful Representations of Life (LMRL

Interpretable convolutional neural networks for effective translation initiation site prediction

Thanks to rapidly evolving sequencing techniques, the amount of genomic data at our disposal is growing increasingly large. Determining the gene structure is a fundamental requirement to effectively interpret gene function and regulation. An important part in that determination process is the identification of translation initiation sites. In this paper, we propose a novel approach for automatic prediction of translation initiation sites, leveraging convolutional neural networks that allow for automatic feature extraction. Our experimental results demonstrate that we are able to improve the state-of-the-art approaches with a decrease of 75.2% in false positive rate and with a decrease of 24.5% in error rate on chosen datasets. Furthermore, an in-depth analysis of the decision-making process used by our predictive model shows that our neural network implicitly learns biologically relevant features from scratch, without any prior knowledge about the problem at hand, such as the Kozak consensus sequence, the influence of stop and start codons in the sequence and the presence of donor splice site patterns. In summary, our findings yield a better understanding of the internal reasoning of a convolutional neural network when applying such a neural network to genomic data

ToxDL : deep learning using primary structure and domain embeddings for assessing protein toxicity

Motivation: Genetically engineering food crops involves introducing proteins from other species into crop plant species or modifying already existing proteins with gene editing techniques. In addition, newly synthesized proteins can be used as therapeutic protein drugs against diseases. For both research and safety regulation purposes, being able to assess the potential toxicity of newly introduced/synthesized proteins is of high importance. Results: In this study, we present ToxDL, a deep learning-based approach for in silico prediction of protein toxicity from sequence alone. ToxDL consists of (i) a module encompassing a convolutional neural network that has been designed to handle variable-length input sequences, (ii) a domain2vec module for generating protein domain embeddings and (iii) an output module that classifies proteins as toxic or non-toxic, using the outputs of the two aforementioned modules. Independent test results obtained for animal proteins and cross-species transferability results obtained for bacteria proteins indicate that ToxDL outperforms traditional homology-based approaches and state-of-the-art machine-learning techniques. Furthermore, through visualizations based on saliency maps, we are able to verify that the proposed network learns known toxic motifs. Moreover, the saliency maps allow for directed in silico modification of a sequence, thus making it possible to alter its predicted protein toxicity