Creating story maps for learning purposes: The Black Death Atlas

In the current technological context new forms of mapmaking emerge. An increasingly common one produces story maps, maps that are shown with synchronized explanatory text, to visualize events in a spatial context. Story maps could be defined as the explicit display of such spatial and temporal landmarks on the grounds that a story is constructed. In this paper we present a story map oriented to pedagogical purposes. We have compiled an atlas displaying the expansion of the Black Death in Europe between 1346 and 1347, when the largest epidemic outbreak in the History of Europe ravaged the continent. To depict this event, we have used CartoDB, Odyssey and some other Web interactive tools to create eight interactive story maps gathered in an online atlas. The work was made in the frame of an end-of-degree Project (Geomatics Engineering, in Universidad Politécnica de Madrid). By now, it can be found in: http://clarar92.wix.com/atlasdelapestenegr

A defect in myoblast fusion underlies Carey-Fineman-Ziter syndrome

Multinucleate cellular syncytial formation is a hallmark of skeletal muscle differentiation. Myomaker, encoded by Mymk (Tmem8c), is a well-conserved plasma membrane protein required for myoblast fusion to form multinucleated myotubes in mouse, chick, and zebrafish. Here, we report that autosomal recessive mutations in MYMK (OMIM 615345) cause Carey-Fineman-Ziter syndrome in humans (CFZS; OMIM 254940) by reducing but not eliminating MYMK function. We characterize MYMK-CFZS as a congenital myopathy with marked facial weakness and additional clinical and pathologic features that distinguish it from other congenital neuromuscular syndromes. We show that a heterologous cell fusion assay in vitro and allelic complementation experiments in mymk knockdown and mymk insT/insT zebrafish in vivo can differentiate between MYMK wild type, hypomorphic and null alleles. Collectively, these data establish that MYMK activity is necessary for normal muscle development and maintenance in humans, and expand the spectrum of congenital myopathies to include cell-cell fusion deficits

Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant

Gene regulatory networks (GRNs) consist of different molecular interactions that closely work together to establish proper gene expression in time and space. Especially in higher eukaryotes, many questions remain on how these interactions collectively coordinate gene regulation. We study high quality GRNs consisting of undirected protein-protein, genetic and homologous interactions, and directed protein-DNA, regulatory and miRNA-mRNA interactions in the worm Caenorhabditis elegans and the plant Ara-bidopsis thaliana. Our data-integration framework integrates interactions in composite network motifs, clusters these in biologically relevant, higher-order topological network motif modules, overlays these with gene expression profiles and discovers novel connections between modules and regulators. Similar modules exist in the integrated GRNs of worm and plant. We show how experimental or computational methodologies underlying a certain data type impact network topology. Through phylogenetic decomposition, we found that proteins of worm and plant tend to functionally interact with proteins of a similar age, while at the regulatory level TFs favor same age, but also older target genes. Despite some influence of the duplication mode difference, we also observe at the motif and module level for both species a preference for age homogeneity for undirected and age heterogeneity for directed interactions. This leads to a model where novel genes are added together to the GRNs in a specific biological functional context, regulated by one or more TFs that also target older genes in the GRNs. Overall, we detected topological, functional and evolutionary properties of GRNs that are potentially universal in all species

NPInter: the noncoding RNAs and protein related biomacromolecules interaction database

The noncoding RNAs and protein related biomacromolecules interaction database (NPInter; or ) is a database that documents experimentally determined functional interactions between noncoding RNAs (ncRNAs) and protein related biomacromolecules (PRMs) (proteins, mRNAs or genomic DNAs). NPInter intends to provide the scientific community with a comprehensive and integrated tool for efficient browsing and extraction of information on interactions between ncRNAs and PRMs. Beyond cataloguing details of these interactions, the NPInter will be useful for understanding ncRNA function, as it adds a very important functional element, ncRNAs, to the biomolecule interaction network and sets up a bridge between the coding and the noncoding kingdoms

Transfer learning for multi-channel time-series Human Activity Recognition

Abstract for the PHD Thesis Transfer Learning for Multi-Channel Time-Series Human Activity Recognition Methods of human activity recognition (HAR) have been developed for the purpose of automatically classifying recordings of human movements into a set of activities. Capturing, evaluating, and analysing sequential data to recognise human activities accurately is critical for many applications in pervasive and ubiquitous computing applications, e.g., in applications such as mobile- or ambient-assisted living, smart-homes, activities of daily living, health support and rehabilitation, sports, automotive surveillance, and industry 4.0. For example, HAR is particularly interesting for optimisation in those industries where manual work remains dominant. HAR takes as inputs signals from videos or from multi-channel time-series, e.g., human joint measurements from marker-based motion capturing systems and inertial measurements measured by wearables or on-body devices. Wearables have become relevant as they extend the potential of HAR beyond constrained or laboratory settings. This thesis focuses on HAR using multi-channel time-series. Multi-channel Time-Series HAR is, in general, a challenging classification task. This is because human activities and movements show a large variation. Humans carry out in similar manner activities that are semantically very distinctive; conversely, they carry out similar activities in many different ways. Furthermore, multi-channel Time-Series HAR datasets suffer from the class unbalance problem, with more samples of certain activities than others. This problem strongly depends on the annotation. Moreover, there are non-standard definitions of human activities for annotation. Methods based on Deep Neural Networks (DNNs) are prevalent for Multi-channel Time-Series HAR. Nevertheless, the performance of DNNs has not significantly increased compared to as other fields such as image classification or segmentation. DNNs present a low sample efficiency as they learn the temporal structure from activities completely from data. Considering supervised DNNs, the scarcity of annotated data is the primary concern. Annotated data from human behaviour is scarce and costly to obtain. The annotation process demands enormous resources. Additionally, annotation reliability varies because they can be subject to human errors or unclear and non-elaborated annotation protocols. Transfer learning has been used to cope with a limited amount of annotated data, overfitting, zero-shot learning or classification of unseen human activities, and the class-unbalance problem. Transfer learning can alleviate the problem of scarcity of annotated data. Learnt parameters and feature representations from a specific source domain are transferred to a target domain. Transfer learning extends the usability of large annotated data from source domains to related problems. This thesis proposes a general transfer learning approach to improve automatic multi-channel Time-Series HAR. The proposed transfer learning method combines a semantic attribute representation of activities and a specific deep neural network. It handles situations where the source and target domains differ, i.e., the sensor space and the set of activities change, without needing a large amount of annotated data from the target domain. The method considers different levels of transferability. First, an architecture handles a variate of dataset configurations in regard to the number of devices and their type; it creates fixed-size representations of sensor recordings that are representative of the human limbs. These networks will process sequences of movements from the human limbs, either from poses or inertial measurements. Second, it introduces a search of semantic attribute representations that favourably represent signal segments for recognising human activities in unknown scenarios, as they only consider annotations of activities, and they lack human-annotated semantic attributes. And third, it covers transferability from data of a variety of source datasets. The method takes advantage of a large human-pose dataset as a source domain, which is created during the develop of this thesis. Furthermore, synthetic-inertial measurements will be derived from sequences of human poses either from a marker-based motion capturing system or video-based HAR and pose-based HAR datasets. The latter will specifically use the annotations of pixel-coordinate of human poses as multi-channel time-series data. Real inertial measurements and these synthetic measurements will then be deployed as a source domain for parameter transfer learning. Experimentation on different target datasets demonstrates that the proposed transfer learning method improves performance, most evidently when deploying a proportion of their training material. This outcome suggests that the temporal convolutional filters are rather general as they learn local temporal relations of human movements related to the semantic attributes, independent of the number of devices and their type. A human-limb-oriented deep architecture and an evolutionary algorithm provide an out-of-the-shelf predictor of semantic attributes that can be deployed directly on a new target scenario. Very related problems can directly be addressed by manually giving the attribute-to-activity relations without the need for a search throughout an evolutionary algorithm. Besides, the learnt convolutional filters are activity class dependent. Hence, the classification performance on the activities shared among the datasets improves

Evaluating Large Language Models for Health-Related Text Classification Tasks with Public Social Media Data

Large language models (LLMs) have demonstrated remarkable success in NLP tasks. However, there is a paucity of studies that attempt to evaluate their performances on social media-based health-related natural language processing tasks, which have traditionally been difficult to achieve high scores in. We benchmarked one supervised classic machine learning model based on Support Vector Machines (SVMs), three supervised pretrained language models (PLMs) based on RoBERTa, BERTweet, and SocBERT, and two LLM based classifiers (GPT3.5 and GPT4), across 6 text classification tasks. We developed three approaches for leveraging LLMs for text classification: employing LLMs as zero-shot classifiers, us-ing LLMs as annotators to annotate training data for supervised classifiers, and utilizing LLMs with few-shot examples for augmentation of manually annotated data. Our comprehensive experiments demonstrate that employ-ing data augmentation using LLMs (GPT-4) with relatively small human-annotated data to train lightweight supervised classification models achieves superior results compared to training with human-annotated data alone. Supervised learners also outperform GPT-4 and GPT-3.5 in zero-shot settings. By leveraging this data augmentation strategy, we can harness the power of LLMs to develop smaller, more effective domain-specific NLP models. LLM-annotated data without human guidance for training light-weight supervised classification models is an ineffective strategy. However, LLM, as a zero-shot classifier, shows promise in excluding false negatives and potentially reducing the human effort required for data annotation. Future investigations are imperative to explore optimal training data sizes and the optimal amounts of augmented data

CGGBP1 regulates cell cycle in cancer cells

<p>Abstract</p> <p>Background</p> <p>CGGBP1 is a CGG-triplet repeat binding protein, which affects transcription from CGG-triplet-rich promoters such as the FMR1 gene and the ribosomal RNA gene clusters. Earlier, we reported some previously unknown functions of CGGBP1 in gene expression during heat shock stress response. Recently we had found CGGBP1 to be a cell cycle regulatory midbody protein required for normal cytokinetic abscission in normal human fibroblasts, which have all the cell cycle regulatory mechanisms intact.</p> <p>Results</p> <p>In this study we explored the role of CGGBP1 in the cell cycle in various cancer cell lines. CGGBP1 depletion by RNA interference in tumor-derived cells caused an increase in the cell population at G0/G1 phase and reduced the number of cells in the S phase. CGGBP1 depletion also increased the expression of cell cycle regulatory genes CDKN1A and GAS1, associated with reductions in histone H3 lysine 9 trimethylation in their promoters. By combining RNA interference and genetic mutations, we found that the role of CGGBP1 in cell cycle involves multiple mechanisms, as single deficiencies of CDKN1A, GAS1 as well as TP53, INK4A or ARF failed to rescue the G0/G1 arrest caused by CGGBP1 depletion.</p> <p>Conclusions</p> <p>Our results show that CGGBP1 expression is important for cell cycle progression through multiple parallel mechanisms including the regulation of CDKN1A and GAS1 levels.</p