VariVis: a visualisation toolkit for variation databases

<p>Abstract</p> <p>Background</p> <p>With the completion of the Human Genome Project and recent advancements in mutation detection technologies, the volume of data available on genetic variations has risen considerably. These data are stored in online variation databases and provide important clues to the cause of diseases and potential side effects or resistance to drugs. However, the data presentation techniques employed by most of these databases make them difficult to use and understand.</p> <p>Results</p> <p>Here we present a visualisation toolkit that can be employed by online variation databases to generate graphical models of gene sequence with corresponding variations and their consequences. The VariVis software package can run on any web server capable of executing Perl CGI scripts and can interface with numerous Database Management Systems and "flat-file" data files. VariVis produces two easily understandable graphical depictions of any gene sequence and matches these with variant data. While developed with the goal of improving the utility of human variation databases, the VariVis package can be used in any variation database to enhance utilisation of, and access to, critical information.</p

The fitness cost of mis-splicing is the main determinant of alternative splicing patterns

Background Most eukaryotic genes are subject to alternative splicing (AS), which may contribute to the production of protein variants or to the regulation of gene expression via nonsense-mediated messenger RNA (mRNA) decay (NMD). However, a fraction of splice variants might correspond to spurious transcripts and the question of the relative proportion of splicing errors to functional splice variants remains highly debated. Results We propose a test to quantify the fraction of AS events corresponding to errors. This test is based on the fact that the fitness cost of splicing errors increases with the number of introns in a gene and with expression level. We analyzed the transcriptome of the intron-rich eukaryote Paramecium tetraurelia. We show that in both normal and in NMD-deficient cells, AS rates strongly decrease with increasing expression level and with increasing number of introns. This relationship is observed for AS events that are detectable by NMD as well as for those that are not, which invalidates the hypothesis of a link with the regulation of gene expression. Our results show that in genes with a median expression level, 92–98% of observed splice variants correspond to errors. We observed the same patterns in human transcriptomes and we further show that AS rates correlate with the fitness cost of splicing errors. Conclusions These observations indicate that genes under weaker selective pressure accumulate more maladaptive substitutions and are more prone to splicing errors. Thus, to a large extent, patterns of gene expression variants simply reflect the balance between selection, mutation, and drift

Multimodal integration of longitudinal noninvasive diagnostics for survival prediction in immunotherapy using deep learning.

Immunotherapies have revolutionized the landscape of cancer treatments. However, our understanding of response patterns in advanced cancers treated with immunotherapy remains limited. By leveraging routinely collected noninvasive longitudinal and multimodal data with artificial intelligence, we could unlock the potential to transform immunotherapy for cancer patients, paving the way for personalized treatment approaches. In this study, we developed a novel artificial neural network architecture, multimodal transformer-based simple temporal attention (MMTSimTA) network, building upon a combination of recent successful developments. We integrated pre- and on-treatment blood measurements, prescribed medications, and CT-based volumes of organs from a large pan-cancer cohort of 694 patients treated with immunotherapy to predict mortality at 3, 6, 9, and 12 months. Different variants of our extended MMTSimTA network were implemented and compared to baseline methods, incorporating intermediate and late fusion-based integration methods. The strongest prognostic performance was demonstrated using a variant of the MMTSimTA model with area under the curves of 0.84 ± 0.04, 0.83 ± 0.02, 0.82 ± 0.02, 0.81 ± 0.03 for 3-, 6-, 9-, and 12-month survival prediction, respectively. Our findings show that integrating noninvasive longitudinal data using our novel architecture yields an improved multimodal prognostic performance, especially in short-term survival prediction. Our study demonstrates that multimodal longitudinal integration of noninvasive data using deep learning may offer a promising approach for personalized prognostication in immunotherapy-treated cancer patients

Constrained pre-equalization accounting for multi-path fading emulated using large RC networks: applications to wireless and photonics communications

Multi-path propagation is modelled assuming a multi-layer RC network with randomly allocated resistors and capacitors to represent the transmission medium. Due to frequency-selective attenuation, the waveforms associated with each propagation path incur path-dependent distortion. A pre-equalization procedure that takes into account the capabilities of the transmission source as well as the transmission properties of the medium is developed. The problem is cast within a Mixed Integer Linear Programming optimization framework that uses the developed nominal RC network model, with the excitation waveform customized to optimize signal fidelity from the transmitter to the receiver. The objective is to match a Gaussian pulse input accounting for frequency regions where there would be pronounced fading. Simulations are carried out with different network realizations in order to evaluate the sensitivity of the solution with respect to changes in the transmission medium mimicking the multi-path propagation. The proposed approach is of relevance where equalization techniques are difficult to implement. Applications are discussed within the context of emergent communication modalities across the EM spectrum such as light percolation as well as emergent indoor communications assuming various modulation protocols or UWB schemes as well as within the context of space division multiplexing

3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction

Past few years have witnessed the prevalence of deep learning in many application scenarios, among which is medical image processing. Diagnosis and treatment of brain tumors requires an accurate and reliable segmentation of brain tumors as a prerequisite. However, such work conventionally requires brain surgeons significant amount of time. Computer vision techniques could provide surgeons a relief from the tedious marking procedure. In this paper, a 3D U-net based deep learning model has been trained with the help of brain-wise normalization and patching strategies for the brain tumor segmentation task in the BraTS 2019 competition. Dice coefficients for enhancing tumor, tumor core, and the whole tumor are 0.737, 0.807 and 0.894 respectively on the validation dataset. These three values on the test dataset are 0.778, 0.798 and 0.852. Furthermore, numerical features including ratio of tumor size to brain size and the area of tumor surface as well as age of subjects are extracted from predicted tumor labels and have been used for the overall survival days prediction task. The accuracy could be 0.448 on the validation dataset, and 0.551 on the final test dataset.Comment: Third place award of the 2019 MICCAI BraTS challenge survival task [BraTS 2019](https://www.med.upenn.edu/cbica/brats2019.html

Sequence Motifs in MADS Transcription Factors Responsible for Specificity and Diversification of Protein-Protein Interaction

Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and network evolution

Ultra-high bandwidth quantum secured data transmission

Quantum key distribution (QKD) provides an attractive means for securing communications in optical fibre networks. However, deployment of the technology has been hampered by the frequent need for dedicated dark fibres to segregate the very weak quantum signals from conventional traffic. Up until now the coexistence of QKD with data has been limited to bandwidths that are orders of magnitude below those commonly employed in fibre optic communication networks. Using an optimised wavelength divisional multiplexing scheme, we transport QKD and the prevalent 100 Gb/s data format in the forward direction over the same fibre for the first time. We show a full quantum encryption system operating with a bandwidth of 200 Gb/s over a 100 km fibre. Exploring the ultimate limits of the technology by experimental measurements of the Raman noise, we demonstrate it is feasible to combine QKD with 10 Tb/s of data over a 50 km link. These results suggest it will be possible to integrate QKD and other quantum photonic technologies into high bandwidth data communication infrastructures, thereby allowing their widespread deployment

Current controversies in TNM for the radiological staging of rectal cancer and how to deal with them: results of a global online survey and multidisciplinary expert consensus

Objectives: To identify the main problem areas in the applicability of the current TNM staging system (8th ed.) for the radiological staging and reporting of rectal cancer and provide practice recommendations on how to handle them. Methods: A global case-based online survey was conducted including 41 image-based rectal cancer cases focusing on various items included in the TNM system. Cases reaching < 80% agreement among survey respondents were identified as problem areas and discussed among an international expert panel, including 5 radiologists, 6 colorectal surgeons, 4 radiation oncologists, and 3 pathologists. Results: Three hundred twenty-one respondents (from 32 countries) completed the survey. Sixteen problem areas were identified, related to cT staging in low-rectal cancers, definitions for cT4b and cM1a disease, definitions for mesorectal fascia (MRF) involvement, evaluation of lymph nodes versus tumor deposits, and staging of lateral lymph nodes. The expert panel recommended strategies on how to handle these, including advice on cT-stage categorization in case of involvement of different layers of the anal canal, specifications on which structures to include in the definition of cT4b disease, how to define MRF involvement by the primary tumor and other tumor-bearing structures, how to differentiate and report lymph nodes and tumor deposits on MRI, and how to anatomically localize and stage lateral lymph nodes. Conclusions: The recommendations derived from this global survey and expert panel discussion may serve as a practice guide and support tool for radiologists (and other clinicians) involved in the staging of rectal cancer and may contribute to improved consistency in radiological staging and reporting. Key Points: • Via a case-based online survey (incl. 321 respondents from 32 countries), we identified 16 problem areas related to the applicability of the TNM staging system for the radiological staging and reporting of rectal cancer. • A multidisciplinary panel of experts recommended strategies on how to handle these problem areas, including advice on cT-stage categorization in case of involvement of different layers of the anal canal, specifications on which structures to include in the definition of cT4b disease, how to define mesorectal fascia involvement by the primary tumor and other tumor-bearing structures, how to differentiate and report lymph nodes and tumor deposits on MRI, and how to anatomically localize and stage lateral lymph nodes. • These recommendations may serve as a practice guide and support tool for radiologists (and other clinicians) involved in the staging of rectal cancer and may contribute to improved consistency in radiological staging and reporting