Miracle-dz/UniFAR-FTUs huggingface dataset:
Center of Mass Estimation for Impaired Gait Assessment Using Inertial Measurement Units

Injury or disease often compromise walking dynamics and negatively impact quality of life and independence. Assessing methods to restore or improve pathological gait can be expedited by examining a global parameter that reflects overall musculoskeletal control. Center of mass (CoM) kinematics follow well-defined trajectories during unimpaired gait, and change predictably with various gait pathologies. We propose a method to estimate CoM trajectories from inertial measurement units (IMUs) using a bidirectional Long Short-Term Memory neural network to evaluate rehabilitation interventions and outcomes. Five non-disabled volunteers participated in a single session of various dynamic walking trials with IMUs mounted on various body segments. A neural network trained with data from four of the five volunteers through a leave-one-subject out cross validation estimated the CoM with average root mean square errors (RMSEs) of 1.44cm, 1.15cm, and 0.40cm in the mediolateral (ML), anteroposterior (AP), and inferior/superior (IS) directions respectively. The impact of number and location of IMUs on network prediction accuracy was determined via principal component analysis. Comparing across all configurations, three to five IMUs located on the legs and medial trunk were the most promising reduced sensor sets for achieving CoM estimates suitable for outcome assessment. Lastly, the networks were tested on data from an individual with hemiparesis with the greatest error increase in the ML direction, which could stem from asymmetric gait. These results provide a framework for assessing gait deviations after disease or injury and evaluating rehabilitation interventions intended to normalize gait pathologies.
Cognitive Retrieve: Empowering Document Retrieval with Semantics and Domain Specific Knowledge Graph

As the data landscape continues to expand, the task of identifying relevant documents becomes increasingly complex, especially when dealing with diverse and varied data sources. Traditional keyword-based search systems struggle to capture the subtle contextual meaning of search queries. Semantic-based search, leveraging open data knowledge graphs, offers a solution by understanding contextual meaning. However, its effectiveness relies heavily on the quality and completeness of the underlying data used to define these semantics. However, incomplete data can lead to spurious results and a lack of relevance in the retrieved documents. To bridge this gap between user search interest and retrieval outcomes, we propose integrating domain-specific alignment into the search process. Our research aims to achieve this through the development of a semantic-driven data processing pipeline, laying the foundation for seamless semantic-oriented retrieval. This approach includes metadata extraction, considering domain-specific keywords and structural metadata from heterogeneous data sources. We enhance metadata by identifying latent terms using language models. Furthermore, we incorporate latent concepts and domain-specific information gathered from domain experts into a special knowledge graph construct-a ‘concept graph’. Our primary focus is on identifying relevant concepts from this graph, aligning with semantic and contextual aspects of the specified search intent. Our proposed document retrieval system, which combines the concept graph with semantics, is implemented using data from the Government of Karnataka, India. This approach addresses the administrative need to extract relevant documents from data silos, offering an alternative approach to traditional methods. Extensive evaluations demonstrate the proposed system’s superior performance in terms of true positive results compared to baseline systems like Lucene, Elasticsearch, and Doc2Vec.
New methods to investigate the GnRH pulse generator

The exact neural construct underlying the dynamic secretion of gonadotrophin-releasing hormone (GnRH) has only recently been identified despite the detection of multiunit electrical activity volleys associated with pulsatile luteinising hormone (LH) secretion four decades ago. Since the discovery of kisspeptin/neurokinin B/dynorphin neurons in the mammalian hypothalamus, there has been much research into the role of this neuronal network in controlling the oscillatory secretion of gonadotrophin hormones. In this review, we provide an update of the progressive application of cutting-edge techniques combined with mathematical modelling by the neuroendocrine community, which are transforming the functional investigation of the GnRH pulse generator. Understanding the nature and function of the GnRH pulse generator can greatly inform a wide range of clinical studies investigating infertility treatments.
New developments in spatial econometric modelling

ABSTRACT This special issue brings together five methodological contributions and responses to the 16th World Conference of the Spatial Econometric Association held in Warsaw, Poland, in June 2022. Each paper presents a new technique to provide better answers to the different stages of spatial econometric modelling. The first paper develops a method for determining principal components that capture both spatial and temporal dependence. The second paper provides five statistics to measure the extent to which spatial lags are collectively or individually significant in a spatial Durbin model. The third paper provides a roadmap to guide scholars through the specification search to explore spatial dependence underlying regions’ resilience. The fourth paper employs the Akaike information criterion to determine the best performing k-nearest neighbour matrix in a spatial econometric model. The fifth paper explores a methodology to determine the best performing spatial weight matrix based on a set of covariates rather than geographical distance-based measures.
Multi-model Estimators and Ensemble-based Regressors for Predicting Video Memorability

The need to organize prevalent multimedia in our day-to-day activities, presents us various aspects of video importance to be taken into consideration, like video aesthetics and interestingness. Memorability based organization of media is highly effective in cases where media is required to have a long positive retainability in one’s memory, like in the field of advertising or educational content creation. Keeping our target group in mind, we have decided to pursue an ensemble-based approach for our model for the task of predicting media memorability in aide to the Benchmarking Initiative for Multimedia Evaluation’s list of tasks for the MediaEval 2022 Workshop. Pre-defined ensemble models are versatile enough to incorporate other pre-defined regressors and ensemble models as base estimators and build on the knowledge accumulated by them. Video-level features like the C3D were chosen, and the regressors and ensemblers were trained on these features with parameter optimization.
Representational Learning for the Detection of COVID Related Conspiracy Spreaders in Online Platforms

The approach known as representational learning is a set of techniques that allows the automatic discovery of features required for a machine learning task from raw data. In recent years, the application of these techniques to graphs has shown promising results in node classification tasks. This work applies representational learning to identify users that share COVID-related conspiracy theories, using their interactions with peers as the main features for the classification algorithms. To do so, Node2vec and FastRP were used to learn numeric representations, i.e. embeddings, of the users. Then, Random Forest and XGBoost were used for the downstream classification task. In addition, a pseudo-labeling procedure was applied. The experimentation shows that using interaction data for the classification task achieves better performance compared to classifying using only node attributes. Moreover, FastRP achieve better performance compared to Node2vec. However, pseudo-labeling does not improve the performance of the models at all. Finally, we reject the inclusion of "cannot determine" labels in our model, as they prove to be detrimental.
Addressing Generalization Failure in Deep Detection Models for Fishing Trawler Video Analytics

A problem with supervised machine learning algorithms is how to precisely predict outcome values for previously unseen data. In this paper, we evaluate conventional detection models trained on the Njord commercial fishing video surveillance dataset in this generalization context. Our results show that these models fail to generalize to a test-set consisting of samples with challenging lighting-and weather-conditions. To address this, a novel distributional-shift detector is introduced, exhibiting good performance and outperforming competing methods by a considerable margin. 1
Toxicological Profiling of Potential Shikimate Kinase Inhibitors Against Mycobacterium tuberculosis

Over the last decade, Mycobacterium tuberculosis has mutated into a putative ‘superbug’, as treatments against it have failed due to increasing antimicrobial resistance. As a result, the rising incidence of multidrug-resistant tuberculosis (MDR-TB) is posing a significant public health threat, thus, the need to develop effective drugs for MDR-TB has become an urgent priority. To identify new drug candidates for the treatment of MDR-TB, the present study was based on mycobacterial shikimate kinase (MtSK) as the pharmacological target. One hundred potential MtSK inhibitors were identified from literature and database searches to identify compounds that were designed to specifically function as MtSK antagonists. The ADME properties of these compounds were evaluated by using the SwissADME web tool. ProTox-II software was also used to investigate any potential endocrine disrupting effects, mediated through their interaction with oestrogenic and/or androgenic receptors. This study also aimed to predict LD50 values of potential drug candidates that would be active against the standard H37Rv strain of M. tuberculosis, by using the ProTox-II in silico tool. The molecules for which no structural hazard alerts were identified with these software tools were further subjected to molecular docking analyses and molecular dynamic simulations to estimate their ability to interact with the MtSK enzyme. Preliminary results from SwissADME indicated that 30 molecules were drug-like, due to their physicochemical and pharmacokinetic properties. However, subsequent analysis with ToxTree and ProTox-II indicated that only three of these 30 drug-like molecules were suitable for taking forward into further in vitro experiments. This study, which is based on the use of commonly used open-source in silico tools, identified new MtSK ligands for potential use in the development of new drugs for the therapeutic management of tuberculosis. An initial prediction of their safety profile was also generated.
Comparative analysis of scientific output of BRIC countries

Based on data for the period 1980-2009 downloaded from the Scopus database, an attempt is made to analyse the growth of publication share in BRIC countries (Brazil, Russia, India and China) vis-a-vis the United States in terms of world publications output and emerging areas and disciplines during 1980–2009. Also, comparative estimates are made using a competition model to analyse the emerging patterns of scientific publication in the BRIC region. Results indicate that China may be an emerging leader in scientific publication followed by India among the BRIC countries.
On the (Im)Possibility of Estimating Various Notions of Differential Privacy (short paper)

We analyze to what extent final users can infer information about the level of protection of their data when the data obfuscation mechanism is a priori unknown to them (the so-called “black-box" scenario). In particular, we delve into the investigation of two notions of local differential privacy (LDP), namely 𝜀 -LDP and Rényi LDP. On one hand, we prove that, without any assumption on the underlying distributions, it is not possible to have an algorithm able to infer the level of data protection with provable guarantees. On the other hand, we demonstrate that, under reasonable assumptions (namely, Lipschitzness of the involved densities on a closed interval), such guarantees exist and can be achieved by a simple histogram-based estimator.
An Ensemble Approach Towards Correlating Articles and their Corresponding Images

This paper presents a novel approach for the ranking of images for news articles as part of the the “NewsImages: Relating news articles and images” task, in order to better understand the relationship between the textual and visual content of news articles. The proposed approach combines the entity relationship and the contextual similarity between text and image by summarising both modalities into text annotations. The text annotations are generated from the news articles using Named Entity Recognition and Part of Speech tagging. Image annotations comprise of objects and labels generated from the image using the Google Vision API. The text and image annotations were further expanded by generating synsets and enriched using the wikipedia API. The analysis of the results has been carried out on the training dataset provided, and the five different results generated using five configurations of the proposed approach are compared.
Pasa: leveraging population pangenome graph to scaffold prokaryote genome assemblies

Abstract Whole genome sequencing has increasingly become the essential method for studying the genetic mechanisms of antimicrobial resistance and for surveillance of drug-resistant bacterial pathogens. The majority of bacterial genomes sequenced to date have been sequenced with Illumina sequencing technology, owing to its high-throughput, excellent sequence accuracy, and low cost. However, because of the short-read nature of the technology, these assemblies are fragmented into large numbers of contigs, hindering the obtaining of full information of the genome. We develop Pasa, a graph-based algorithm that utilizes the pangenome graph and the assembly graph information to improve scaffolding quality. By leveraging the population information of the bacteria species, Pasa is able to utilize the linkage information of the gene families of the species to resolve the contig graph of the assembly. We show that our method outperforms the current state of the arts in terms of accuracy, and at the same time, is computationally efficient to be applied to a large number of existing draft assemblies.
International Conference on Industrial Informatics ( INDIN ) Perth , Australia 10-12 August 2005

The advances in information and communication technologies coupled with increased knowledge about genes and proteins have opened new perspectives for study of protein complexes. There is a growing need to integrate the knowledge about various protein complexes for effective disease prevention mechanisms, individualized medicines and treatments and other accepts of healthcare. In this paper we propose a Protein Ontology that will handle the following computational challenges in the area Proteomics and systems biology in general: (1) It will provide more accurate interpretations and associations as conclusions are based on Data and Semantics. (2) It will make it possible to study relationships among proteins, protein folding, behaviour of protein under various environments, and most importantly cellular function of protein. This Protein Ontology is a unified terminology description integrating various protein database schemas and will provide a easier way to predict and understand proteins. Index Terms — Protein Ontology, Protein Informatics, Biomedical Ontologies, Biomedical Systems, Data Integration
Video Memorability Prediction using Deep Features and Loss-based Memorability Distribution Estimation

In this paper, we address the video memorability prediction problem, which was a part of MediaEval 2022. This problem involves learning a model that maps the video content to a global memorability score. However, this value is still related to uncertain factors, including time and human subjectivity. Hence, we focus in this work mainly on modeling the subjective nature of the memorability experiments. Our approach relied on predicting the video memorability based on the maximum likelihood estimation of the Bernoulli distribution of the success variable of each video. To evaluate our approach, preliminary experiments were performed on the Memento10k dataset. First promising results were achieved by using a simple fusion of the visual and textual pre-trained features as input.
Shh from mossy cells contributes to preventing NSC pool depletion after seizure-induced neurogenesis and in aging

Epileptic seizures induce aberrant neurogenesis from resident neural stem cells (NSCs) in the dentate gyrus of the adult mouse hippocampus, which has been implicated in depletion of the NSC pool and impairment of hippocampal function. However, the mechanisms regulating neurogenesis after seizures remain unknown. Here, we demonstrate that Sonic hedgehog (Shh) from mossy cells is a major source of Shh signaling activity after seizures, by which mossy cells contribute to seizure-induced neurogenesis and maintenance of the NSC pool. Deletion of Shh from mossy cells attenuates seizure-induced neurogenesis. Moreover, in the absence of Shh from mossy cells, NSCs pool are prematurely depleted after seizure-induced proliferation, and NSCs have impaired self-renewal. Likewise, lack of Shh from mossy cells accelerates age-related decline of the NSC pool with accompanying reduction of self-renewal of NSCs outside the context of pathology such as seizures. Together, our findings indicate that Shh from mossy cells is critical to maintain NSCs and to prevent exhaustion from excessive consumption in aging and after seizures.
Autologous blood products: Leucocyte and Platelets Rich Fibrin (L-PRF) and Platelets Rich Plasma (PRP) gel to promote cutaneous ulcer healing – a systematic review

Objective To summarise evidence on the effectiveness of Platelet-Rich Plasma (PRP) gel and Leucocyte and Platelet Rich Fibrin (L-PRF) gel as agents promoting ulcer healing compared with the standard wound dressing techniques alone. Design Systematic review. Eligibility criteria Individual patient randomised controlled trials on skin ulcers of all types excluding traumatic lesions. Intervention group: treatment with topical application of L-PRF gel or PRP gel to the wound surface. Control group: treatment with standard skin ulcer care using normal saline, normgel or hydrogel dressings. Information sources Medline (Ovid), Excerpta Medica Database (EMBASE), Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Web of Science and manual search of studies from previous systematic reviews and meta-analyses. The papers published from 1946 to 2022 with no restriction on geography and language were included. The last date of the search was performed on 29 August 2022. Data extraction and synthesis Independent reviewers identified eligible studies, extracted data, assessed risk of bias using V.2 of the Cochrane risk-of-bias tool for randomised trials tool and assessed certainty of evidence by using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. Main outcome measures Time to complete healing, proportion healed at a given time and rate of healing. Results Seven studies met the inclusion criteria, five using PRP gel and two using L-PRF gel. One study showed a better proportion of complete healing, three reported reduced meantime to complete healing and five showed improved rate of healing per unit of time in the intervention group. The risk of bias was high across all studies with one exception and the GRADE showed very low certainty of evidence. Conclusion The findings show potential for better outcomes in the intervention; however, the evidence remains inconclusive highlighting a large research gap in ulcer treatment and warrant better-designed clinical trials. PROSPERO registration number CRD42022352418.
The dual role of the DREAM/G2M pathway in non‐tumorigenic immortalization of senescent cells

Anti‐aging and tumorigenesis share common genes and pathways, and thus targeting these genes as part of anti‐aging interventions carries the risk of tumorigenesis. It is essential to understand the gene signatures that balance tumorigenesis and aging. To achieve this goal, we analyzed RNA‐sequencing data from three non‐tumorigenic immortalized cell lines that spontaneously escaped from senescence. By single sample gene set enrichment assay (ssGSEA) and GSEA analysis, we found that both cell growth signaling (E2F targets, MYC targets) and tumor surveillance mechanisms (DNA repair, G2M checkpoint, mitotic spindle) were up‐regulated in all three cell lines, suggesting that these genes are potential signatures for non‐tumorigenic immortalization. Further analysis revealed that the 182 commonly up‐regulated genes in these three cell lines overlapped with the DREAM/G2M pathway, which is known to be the upstream regulator of E2F, Myc targets, DNA repair, G2M checkpoint and mitotic spindle pathways in its cell cycle activation or inhibitory form. By western blotting, quantitative PCR and co‐immunoprecipitation, we verified that both forms of the DREAM pathway are up‐regulated in all three cell lines; this pathway facilitates control of cell cycle progression, supporting a new mechanism for non‐tumorigenic immortalization. Thus, we propose that the DREAM/G2M pathway plays important dual roles with respect to preventing tumorigenesis in the process of immortalization. Our data might serve as the basis for the identification of new signature pathways or gene biomarkers for non‐tumorigenic immortalization, and may aid in the discovery of new targets for tumor‐free anti‐aging drug screening.
Top of the Class: The Importance of Ordinal Rank

This paper examines the long-run impact of ordinal rank during primary school on productivity using comprehensive English administrative data. Identification is obtained from variation in test score distributions across cohorts and subjects, such that the same score relative to the class mean can have different ranks. Conditional on cardinal measures of achievement, being ranked highly during primary school has large effects on secondary school achievement, with the impact of rank being more important for boys than girls. Using additional survey data we find that the development of confidence is the most likely mechanisms for this effect on task-specific productivity.
Protein nanobarcodes enable single-step multiplexed fluorescence imaging

Multiplexed cellular imaging typically relies on the sequential application of detection probes, such as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations.
Semantic Wiki resources and their use for the construction of personalized ontologies

Рассматривается необходимость использования персонифицированных знаний в современных распределенных приложениях и источники получения таких знаний. Проанализированы особенности существующих Wiki-ресурсов и средства их семантической разметки. Предлагается метод использования семантических Wiki-ресурсов для формирования персонифицированных онтологий проблем, решаемых пользователем. Рассматриваются способы применения этих онтологий в интеллектуальных Web-приложениях, в частности, для семантического поиска. Ключевые слова: Wiki-ресурс, онтология, семантический поиск.