Miracle-dz/UniFAR-FTUs huggingface dataset:
Peripheral insulin resistance attenuates cerebral glucose metabolism and impairs working memory in healthy adults

People with insulin resistance are at increased risk for cognitive decline. Insulin resistance has previously been considered primarily a condition of ageing but it is increasingly seen in younger adults. Here, we explore the question that changes in insulin function in early adulthood have both proximal effects, and moderate or even accelerate changes in cerebral metabolism in ageing. Thirty-six younger (mean 27.8 years) and 43 older (mean 75.5) participants completed a battery of tests, including blood sampling, cognitive assessment and a simultaneous PET/MR scan. Cortical thickness and cerebral metabolic rate of glucose were derived for 100 regions and 17 functional networks. Older adults had lower rates of regional cerebral glucose metabolism than younger adults across the brain even after adjusting for lower cortical thickness in older adults. In younger adults, higher insulin resistance was associated with attenuated rates of regional cerebral glucose metabolism, but this was not seen in older adults. The largest effects of insulin resistance in younger adults were in prefrontal, parietal and temporal regions; and in the control, salience ventral attention, default and somatomotor networks. Higher rates of network glucose metabolism were associated with lower reaction time and psychomotor speed. Higher levels of insulin resistance were associated with lower working memory. Our results underscore the importance of insulin sensitivity to brain health and cognitive function across the adult lifespan, even in early adulthood. Significance Statement We show that preventing insulin resistance in early adulthood is important for ensuring efficient fuel supply for the brain and the maintenance of cognitive health across the adult lifespan. Glucose is the primary source of energy for the brain. Decreased glucose metabolism in the brain due to clinically significant levels of insulin resistance is associated with cognitive impairment. Although sub-clinical levels of insulin resistance have also been associated with brain changes, their impact on cerebral metabolism in healthy individuals is unclear. We showed for the first time that – while older adults have lower rates of cerebral metabolism - peripheral insulin resistance attenuates cerebral metabolism more so in healthy younger than healthy older adults, and impairs working memory.
Early onset motor defects and electrographic seizures in a mouse model of the most common mutation in EEF1A2 related neurodevelopmental disorder, E122K

De novo heterozygous missense mutations in EEF1A2, encoding neuromuscular translation-elongation factor eEF1A2, are associated with developmental and epileptic encephalopathies. We used CRISPR/ Cas9 to recapitulate the most common mutation, E122K, in mice. Although E122K/+ mice were not observed to have convulsive seizures, they exhibit frequent electrographic seizures and EEG abnormalities, transient early motor delays and growth defects. Both E122K homozygotes and Eef1a2-null mice develop progressive motor abnormalities phenotypes, with homozygotes reaching humane endpoints by P31. Surprisingly, E122K homozygotes did not exhibit the progressive spinal neurodegeneration which drives the null phenotype. The E122K protein is relatively stable in neurons yet highly unstable in skeletal myocytes, suggesting that the E122K/E122K phenotype is instead driven by loss-of-function in muscle. Importantly, E122K homozygotes developed abnormalities far earlier than nulls, suggesting a toxic gain-of-function. This novel mouse model represents the first animal model of an EEF1A2 missense mutation with face-valid phenotypes and has provided mechanistic insights needed to inform rational treatment design.
CCG-1423-derived compounds reduce global RNA synthesis and inhibit transcriptional responses

Myocardin-related transcription factors (MRTFs) are coactivators of serum response factor (SRF), and thereby regulate cytoskeletal gene expression in response to actin dynamics. MRTFs have also been implicated in heat shock protein (hsp) transcription in fly ovaries, but the mechanisms remain unclear. Here we demonstrate that in mammalian cells, MRTFs are dispensable for hsp gene induction. However, the widely used small molecule inhibitors of MRTF/SRF transcription pathway, derived from CCG-1423, efficiently inhibit hsp gene transcription in both fly and mammalian cells also in absence of MRTFs. Quantifying RNA synthesis and RNA polymerase distribution demonstrates that CCG-1423-derived compounds have a genome-wide effect on transcription. Indeed, tracking nascent transcription at nucleotide resolution reveals that CCG-1423-derived compounds reduce RNA polymerase II elongation, and severely dampen the transcriptional response to heat shock. The effects of CCG-1423-derived compounds therefore extend beyond the MRTF/SRF pathway into nascent transcription, opening novel opportunities for their use in transcription research.
OntoEnricher: A Deep Learning Approach for Ontology Enrichment from Unstructured Text

Information Security in the cyber world is a major cause for concern, with signiﬁcant increase in the number of attack surfaces. Existing information on vulnerabilities, attacks, controls, and advisories available on the web provides an opportunity to represent knowledge and perform security analytics to mitigate some of the concerns. Representing security knowledge in the form of ontology facilitates anomaly detection, threat intelligence, reasoning and relevance attribution of attacks, and many more. This necessitates dynamic and automated enrichment of information security ontologies. However, existing ontology enrichment algorithms based on natural language processing and ML models have issues with the contextual extraction of concepts in words, phrases and sentences. This motivates the need for sequential Deep Learning architectures that traverse through dependency paths in text and extract embedded vulnerabilities, threats, controls, products and other security related concepts and instances from learned path representations. In the proposed approach, Bidirectional LSTMs trained on a large DBpedia dataset and Wikipedia corpus of 2.8 GB along with Universal Sentence Encoder was deployed to enrich ISO 27001 [8] based information security ontology. The approach yielded a test accuracy of over 80% when tested with knocked out concepts from ontology and web page instances to validate the robustness.
PAN-GWES: Pangenome-spanning epistasis and co-selection analysis via de Bruijn graphs

Studies of bacterial adaptation and evolution are hampered by the difficulty of measuring traits such as virulence, drug resistance and transmissibility in large populations. In contrast, it is now feasible to obtain high-quality complete assemblies of many bacterial genomes thanks to scalable and affordable long-read sequencing technology. To exploit this opportunity we introduce a phenotype- and alignment-free method for discovering co-selected and epistatically interacting genomic variation from genome assemblies covering both core and accessory parts of genomes. Our approach uses a compact coloured de Bruijn graph to approximate the intra-genome distances between pairs of loci for a collection of bacterial genomes to account for the impacts of linkage disequilibrium (LD). We demonstrate the versatility of our approach to efficiently identify associations between loci linked with drug resistance and adaptation to the hospital niche in the major human bacterial pathogens Streptococcus pneumoniae and Enterococcus faecalis.
Japanese Encephalitis Virus: A pan-proteome analysis for aggregation propensities and in vitro validation with Capsid anchor and 2K peptide

Japanese encephalitis infection is a vector-borne disease caused by the flavivirus Japanese encephalitis virus (JEV). It is responsible of severe brain infection in humans worldwide. Given the ubiquitous nature of complications and tropism associated with Japanese encephalitis (JE) infection, a holistic understanding of its molecular mechanism is essential. The phenomenon of abnormal protein aggregation into pathogenic amyloids is now increasingly linked to multiple human diseases, also known as Amyloidosis. Most are neurodegenerative disorders but amyloidosis is not restricted to a specific organ or tissue type. The overlap of viral protein aggregation with human pathologies remains limited and it is gaining momentum, especially after the devastating Covid-19 pandemic. Therefore, in this study we have examined the likelihood of aggregation for the entire collection of proteins in JEV. Multiple independent web server tools were employed to scan for potential amyloid prone-regions (APRs), and it was followed by in vitro validation using two JEV transmembrane domains, Capsid anchor and 2K peptides. These synthetic viral peptides were introduced to artificial aggregation-inducing conditions and then analyzed using a different dye-based assays and microscopy methods confirming amyloid-like fibril structure formation. We found these aggregates cytotoxic to human neuronal cell line and membrane damaging to human blood derived RBC’s. The aggregation kinetics of both peptides is enhanced in the presence of artificial membrane models and seeds of self and diabetes hallmark protein Amylin. Our findings thereby strongly suggest the possibility of JEV protein aggregation playing a vital role in its pathogenesis, opening up a broad scope of future study. Also, the interplay between JEV protein aggregation and initiation/progression of other proteopathies is possible and needs further exploration. Graphical Abstract
Users' Effectiveness and Satisfaction for Image Retrieval

This paper presents results from an initial user study exploring the relationship between system effectiveness as quantified by traditional measures such as precision and recall, and users' effectiveness and satisfaction of the results. The tasks involve finding images for recall-based tasks. It was concluded that no direct relationship between system effectiveness and users' performance could be proven (as shown by previous research). People learn to adapt to a system regardless of its effectiveness. This study recommends that a combination of attributes (e.g. system effectiveness, user performance and satisfaction) is a more effective way to evaluate interactive retrieval systems. Results of this study also reveal that users are more concerned with accuracy than coverage of the search results.
A Novel 3-DoF Purely Translational Parallel Mechanism

A novel 3-DoF purely translational parallel mechanism, the Orthotripod, is proposed. It is a variant of the tripod based parallel machine and has a similar architecture to the Orthoglide. In order to reduce the number of passive joints and remove the effect of ease of abrasion of revolute joints, spherical joints are applied in the parallelogram. A mathematic mobility analysis shows the mechanism is indeed 3-DoF purely translational. We optimally design the Orthotripod and the tripod based parallel machine by maximizing the well-conditioned workspace. The optimized Orthotripod possesses a nearly ball-shaped workspace and has much better kinematic performance than the optimized tripod based parallel mechanism. The proposed mechanism is adaptable for machine tool applications
Tangible interactions in control centres for railway traffic management

A current trend in rail industry control centres is to move towards an increase in the number of digital tools to assist operators. Our observations show that the information used by rail controllers is distributed among different interfaces. Operators must assemble all this information themselves to understand the state of the system. This task is often done mentally, resulting in difficult information sharing and a slower decision process. It is in this search for improvement of the operational centres that we realized a prototype of a tangible touch table presented in this article. The first tests are underway and allow us to have rather positive feedback on this technology that operators are not familiar with. The next iterations will focus on refining the prototype and on experimenting with a larger group of users.
Multi-scale, Data-driven and Anatomically Constrained Deep Learning Image Registration for Adult and Fetal Echocardiography

Temporal echocardiography image registration is a basis for clinical quantifications such as cardiac motion estimation, myocardial strain assessments, and stroke volume quantifications. In past studies, deep learning image registration (DLIR) has shown promising results and is consistently accurate and precise, requiring less computational time. We propose that a greater focus on the warped moving image's anatomic plausibility and image quality can support robust DLIR performance. Further, past implementations have focused on adult echocardiography, and there is an absence of DLIR implementations for fetal echocardiography. We propose a framework that combines three strategies for DLIR in both fetal and adult echo: (1) an anatomic shape-encoded loss to preserve physiological myocardial and left ventricular anatomical topologies in warped images; (2) a data-driven loss that is trained adversarially to preserve good image texture features in warped images; and (3) a multi-scale training scheme of a data-driven and anatomically constrained algorithm to improve accuracy. Our tests show that good anatomical topology and image textures are strongly linked to shape-encoded and data-driven adversarial losses. They improve different aspects of registration performance in a non-overlapping way, justifying their combination. Despite fundamental distinctions between adult and fetal echo images, we show that these strategies can provide excellent registration results in both adult and fetal echocardiography using the publicly available CAMUS adult echo dataset and our private multi-demographic fetal echo dataset. Our approach outperforms traditional non-DL gold standard registration approaches, including Optical Flow and Elastix. Registration improvements could be translated to more accurate and precise clinical quantification of cardiac ejection fraction, demonstrating a potential for translation.
Input Data Requirements for Person Identification Information Technology

This paper describes the research of the information technology for person identification, that is based on mathematical models of local-texture descriptors in order to increase efficiency and reduce the probability of identification errors. Analysis of previous studies has shown that the effectiveness of the face recognition and identification algorithm can vary significantly after its appliance to images from different datasets. Therefore, the main purpose of this research is to determine the requirements for face images that will be used in the identification process. During the research, experiments were conducted on several of the most common face image databases, as a result of which the efficiency of the algorithm reached an identification accuracy rate of 95% on images captured under controlled conditions and adjusted within single database. Based on the results of the experiments, the conditions of capturing and parameters of face images were determined, under which the accuracy of identification is the highest.
Towards a real-time possession value framework in ice hockey

Measuring the individual performance of players is an important task in sports analytics. Traditional statistics-based approaches for evaluating hockey players fail to account for context and long-term impact. Recent advances in data gathering have enabled valuing possessions and actions directly to address these issues. This talk describes the implementation of the first real-time possession value framework for ice hockey.
Next-to-leading order QCD corrections to pp→b¯bb¯b + X at the LHC: The quark induced case

: The production of two b¯b pairs is a prominent background for Higgs and New Physics searches in various extensions of the Standard Model. We present here the next-to-leading order QCD corrections to the quark induced subprocess using the GOLEM approach for the virtual corrections. We show that our result considerably improves the prediction and conclude that the inclusion of next-to-leading order effects is indispensable for reliable studies of b¯bb¯b observables in hadronic collisions. titles: Next-to-leading order QCD to pp –> b b_bar b b_bar X at the LHC: the quark induced case Abstract The production of two b ¯ b pairs is a prominent background for Higgs and New Physics searches in various extensions of the Standard Model. We present here the next-to-leading order QCD corrections to the quark induced subprocess using the GOLEM approach for the virtual corrections. We show that our result considerably improves the prediction and conclude that the inclusion of next-to-leading order eﬀects is indispensable for reliable studies of b ¯ bb ¯ b observables in hadronic collisions.
WebLicht-Batch - A Web-Based Interface for Batch Processing Large Input with the WebLicht Workflow Engine

WebLicht is a workﬂow engine that gives researchers access to a well-inhabited space of natural language processing tools that can be combined into tool chains to perform complex natural language analyses. In this paper, we present WebLicht-Batch, a web-based interface to WebLicht’s chainer back-end. WebLicht-Batch helps users to automatically feed large input data, or input data of multiple ﬁles into WebLicht. It disassembles large input into smaller, more digestible sizes, feeds the resulting parts into WebLicht’s pipelining and execution engine
Promoting sustainable urban development in the Palestinian cities

The Palestinian occupied territory encounters consi derable challenges characterized by high population growth rates, scarcity of land, and rapid urbanization. Due to the political instability in the country and the absence of a n effective Palestinian planning institution, urban development and expansion in the Palest inian cities did not follow perceptive planning schemes, which led to a number of con flicts in the structure of cities and adversely affected land use, provision of urban ser vic s, and the quality of the living environment. This research explores the options for future urban development of the Palestinian cities following the concept of sustainable urban grow th. It promotes visions and prospects aimed at fulfilling the needs of the people while c onserving the scarce land and limited natural resources. The notion of a more sustainable ur an development in the Palestinian Territories is promoted through adopting new physic al development patterns. Conceptually, this study aims at exploring the chal lenges and potentials needed for defining sustainable development in the Palestinian co text. Accordingly, several key aspects which steer the physical development in cit ies towards the path of sustainability were investigated together with the conditions and requirements needed to change the common perception of the built environment and prom ote a more sustainable urban development. Through introducing a new visionary wa y of thinking about the Palestinian urban environment, this research seeks to elabo rate new concepts that help to conserve the land in order to promote a better living e vironment for the present and future generations. Both theoretical and empirical analysis were combin ed to guide the methodological approach for tackling the research problem. The cas of Ramallah was analysed highlighting the context, problems, driving forces, pot entials and challenges facing Palestinian cities in general, and Ramallah City in particu lar. A scenario approach was used to describe a sustainable future and outline the means by which change can take place to achieve the challenging targets. The outcome of this dissertation is portrayed in th e form of conceptual proposals suggesting compactness, mixed land use, efficient publ ic transportation, and enhanced green structure as guidelines for sustainable physical de velopment in Palestinian cities. Moreover, this study recommends general strategies, inc ludi g the restructuring of the Palestinian planning institutions, developing a national su stainable development strategy, modifying planning policies, and adapting an integrated p lanning approach, as a basis for promoting and implementing a more sustainable urban de velopment in Palestine.
Evaluation of Driving Performance and User Experience of Different Types of Speedometer

Previously, the classical analog speedometer was the prevalent form of speed indication in cars. With the emergence of new, freely programmable, instrument clusters, its now possible to use any form of visualization to display driving speed. In a driving simulator study with n=17 subjects, we examined the impact of diverse speedometer variants on driving performance, gaze duration, and subjective ratings of user experience and workload. Initial results confirm diverse effects. The conventional speedometer resulted in the shortest eyes off-road times, but was rated worst with respect to UX (hedonic quality). The digital speedometer variant achieved polarizing results while the zoom speedometer performed very well in general. The bracket and linear versions of a speedometer were rated poor in most of the analyzed criteria compared to the alternatives.
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000

In this paper, we present a Bluetooth Low Energy (BLE) based indoor positioning system developed for monitoring the daily living pattern of old people (e.g. people living with dementia) or individuals with disabilities. The proposed sensing system is composed of multiple sensors that are installed in different locations in a home environment. The specific location of the user in the building has been prerecorded into the proposed sensing system that captures the raw Received Signal Strength Indicator (RSSI) from the BLE beacon that is attached on the user. Two methods are proposed to determine the indoor location and the tracking of the users: a trilateration-based method and fingerprinting-based method. Experiments have been carried out in different home environments to verify the proposed system and methods. The results show that our system is able to accurately track the user location in home environments and can track the living patterns of the user which, in turn, may be used to infer the health status of the user. Our results also show that the positions of the BLE beacons on the user and different quality of BLE beacons do not affect the tracking accuracy. INDEX TERMS Bluetooth low energy, living patterns, indoor localization, received signal strength indicator
Social Justice in Student Affairs Graduate Programs: Exploring the Perspectives of Senior Student Affairs Officers

This study highlights how 19 senior student affairs officers (SSAOs) perceived social justice and inclusion (SJI) content in higher education and student affairs (HESA) graduate programs. While SSAOs across identities and institutional types viewed SJI as a programmatic strength, they named tensions between advocacy and administrative work and noted that SJI critiques are less effective when lacking understanding of how to navigate political environments. We include SSAOs’ recommendations for programs and offer implications for practice.