Future Analytics

BackgroundHip-protecting wearable airbags are a novel development, designed for improved efficacy and improved wearer experience compared to traditional hard shell hip protectors. The latter, though effective when worn, are seldom used due to their discomfort to the wearer. Though commercially available, there is limited published data on the efficacy, acceptability or safety of hip-protecting wearable airbags. Here we report on a serious adverse incident (SAE) in one individual wearing one such device.Case presentationA community-dwelling 83 year old female participated in a clinical trial exploring the acceptability and usability of hip-protecting wearable airbags in older adults. She had normal cognition and used a zimmer frame or two walking sticks to mobilise. She had had no falls in the last year but had a marked Fear of Falling (FoF). She had osteoporosis without a fracture history and had centripetal obesity. She was fitted with a commercially-available hip-protecting wearable airbag worn around the waist as a belt with a snap buckle closure, and an elasticated component to ensure fit. Researchers were satisfied that she was able to fit, wear and remove it correctly after instruction and demonstration. On day three of study participation, the participant was walking outdoors and whilst unnoticed to her, the belt was observed by a passer-by to have slipped down to her thighs, ultimately straddling her stride resulting in a fall and distal femur fracture. The participant was found on the ground with the hip-protecting wearable airbag positioned just above her knees, with the buckle still fastened. She required surgical fixation of the fracture and had a prolonged, complicated hospital stay.ConclusionsHip-protecting wearable airbags, though a promising development, may cause serious injury due to slippage risk, particularly in those with centripetal obesity. A manufacturer’s warning should accompany these commercially-available devices to safeguard the user until design change is considered.

Abstract Despite the growing catalog of long noncoding RNAs (lncRNAs), the functional roles of their vast majority in cancer remain poorly defined. To systematically explore lncRNA dependencies in triple-negative breast cancer (TNBC), we compiled a comprehensive annotation by merging GENCODE, BIGTranscriptome, and MiTranscriptome databases and performed a CRISPR-Cas9 deletion screen targeting 1,029 TNBC-enriched lncRNAs. The screen revealed several essential lncRNAs and those modulating doxorubicin sensitivity, with TPL1 emerging among top hits. TPL1 silencing significantly impaired TNBC cell proliferation in both 2D and 3D cultures and reduced invasive capacity in an organ-on-chip model. Transcriptomic and proteomic profiling following TPL1 knockdown revealed downregulation of genes involved in ECM–receptor interaction, focal adhesion, cell migration, and PI3K-Akt signaling. Mechanistically, TPL1 directly interacted with key proteins including EIF4B, MDM2, TARBP2, TLE5, and GTPase RAN, suggesting TPL1 could regulate RNA processing, transcriptional repression, and translation, as well as modulate GTPase signaling pathways. Additionally, TPL1 functioned as a competing endogenous RNA (ceRNA), sequestering miR-10396b-5p, miR-486-3p, and miR-450a-2-3p, among others, thereby modulating expression of pro-tumorigenic targets. Clinically, TPL1 was significantly overexpressed in TNBC tissues, particularly in the BLIS subtype. Collectively, our findings highlight TPL1 as a key regulator of TNBC molecular networks and a promising therapeutic target.

Whitepaper #192 in the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033. Main topics: basic research; infrastructure/workforce/other programmatic. Additional topics: ground-based missions/projects; state of the profession; laboratory space plasma physics; […]

Acetazolamide is an old drug used as an antiepileptic agent, amongst other indications. The drug is seldom used, primarily due to perceived poor efficacy and adverse events. Acetazolamide acts as a noncompetitive inhibitor of carbonic anhydrase, of which there are several subtypes in humans. Acetazolamide causes an acidification of the intracellular and extracellular environments activating acid‐sensing ion channels, and these may account for the anti‐seizure effects of acetazolamide. Other potential mechanisms are modulation of neuroinflammation and attenuation of high‐frequency oscillations. The overall effect increases the seizure threshold in critical structures such as the hippocampus. The evidence for its clinical efficacy was from 12 observational studies of 941 patients. The 50% responder rate was 49%, 20% of patients were rendered seizure‐free, and 30% were noted to have had at least one adverse event. We conclude that the evidence from several observational studies may overestimate efficacy because they lack a comparator; hence, this drug would need further randomized placebo‐controlled trials to assess effectiveness and harm.

Recent advancements in the domain of Network Function Virtualization (NFV), and rollout of next-generation networks have necessitated the requirement for the upkeep of latency-critical application architectures in future networks and communications. While Cloud service providers recognize the evolving mission-critical requirements in latency sensitive verticals such as autonomous driving, multimedia, gaming, telecommunications, and virtual reality, there is a wide gap to bridge the Quality of Service (QoS) constraints for the end-user experience. Most latency-critical services are over-provisioned on all fronts to offer reliability, which is inefficient towards scalability in the long run. To address this, we propose a strategy to model frequent violations on the application level as a multi-output target to enable more complex decision-making in the management of virtualised communication networks. In this work, we utilize data from a real-world deployment to configure and draft a realistic set of Service Level Objectives (SLOs) for a voice based NFV application, and develop a deep neural network based multi-label classification methodology to identify and predict multiple categories of SLO breaches associated with an application state. With this, we aim to gain granular SLA and SLO violation insights, enabling us to study and mitigate their impact and inform precision in drafting proactive scaling policies. We further compare the performance against a set of multi-label compatible machine learning classifiers, and address class imbalance in a multi-label setup. We perform a comprehensive evaluation to assess the performance on example-based, label-based and ranking-based measures, and demonstrate the suitability of deep learning in such a use-case.

The United Nations’ 2030 Sustainable Development Goals have articulated sustainable development requirements at the international level. SDG14: life below water, has in particular, provided a future pathway for sustainable ocean development. With the establishment of this global perspective has come a renewed emphasis on the need for global ocean knowledge production. The 2015 First World Ocean Assessment (FWOA), which was produced by the first cycle of the United Nations’ Regular Process for Global Reporting and Assessment of the State of the Marine Environment, including Socio-economic Aspects, is widely viewed as a primary tool to guiding action on SDG14. This research investigates how effective the FWOA has been at supporting these efforts toward sustainable ocean development. We use a combination of approaches, including document mining, an internationally distributed survey and semi-structured interviews to better understand the impact of the FWOA as well as the interrelated functioning of the Regular Process’ first cycle. While the FWOA was successful in compiling well accepted and credible ocean information, it was unable to generate the impact on sustainable ocean management activities that had originally been expected of it. Funding restrictions, participation issues and political anxieties seemed to derail the first cycle of the Regular Process from initial recommendations and directed the process into unorthodox operations and substantial political control. With the Second World Ocean Assessment (SWOA) well underway, it is imperative that trust is built and social learning is encouraged between participants in the Regular Process.

The paper presents an overview of financial markets surveillance solutions for the detection of various abusive behaviours. Technological advances support high-performance artificial intelligence (AI)-based solutions that help to avoid the drawbacks of legacy solutions. Many AI-based technologies are applied to detect sophisticated fraudulent actions on financial markets. A good approach is outlier or anomaly detection looking for observations that are inconsistent with remainder of the available data. Among the new technologies available are eyeDES, a cutting-edge AI-based technology and platform whose functional components provide market intelligence and unbiased detection of market abuse. It allows the detection of both previously known and completely new abusive behaviours in real time, effectively combining the use of advanced data analytics to enrich the original data space with new Features and anomaly detection to find inconsistent cases. Each case is provided with a score that measures how different that market participant’s activity is from the others, and a number of possible explanations for this. eyeDES is based on a solid and robust reasoning process, and it is an explainable AI technology, because it provides explanations of the rationale behind the decisions.

To assess the effectiveness and safety/tolerability of eslicarbazepine acetate (ESL) monotherapy in clinical practice in Europe. Euro-Esli was a pooled analysis of 14 European clinical practice studies. Responder rate (≥50% seizure frequency reduction) and seizure freedom rate (seizure freedom at least since prior visit) were assessed after 3, 6 and 12months of ESL treatment and at last visit. Adverse events (AEs) and AEs leading to ESL discontinuation were assessed throughout follow-up. A subanalysis was conducted to assess outcomes for patients treated initially with ESL monotherapy and for patients treated at the last visit with ESL monotherapy. ESL was used as monotherapy in 88/2045 (4.3%) patients initially and in 229/1340 (17.1%) patients at the last visit. At 12months, responder and seizure freedom rates were 94.1% and 88.2%, respectively, in patients treated initially with ESL monotherapy, and 93.2% and 77.4%, respectively, in patients treated at the last visit with ESL monotherapy. Corresponding values for patients treated initially with ESL adjunctive therapy were 74.8% and 39.0%, respectively; and for patients treated at the last visit with ESL adjunctive therapy, corresponding values were 70.4% and 25.9%, respectively. Safety and tolerability were generally comparable in patients treated with ESL as monotherapy or adjunctive therapy. The most commonly reported AEs (≥5% of patients in any group) were dizziness, somnolence, instability/ataxia, and fatigue. These clinical practice data support the use of ESL as monotherapy, as well as adjunctive therapy, for focal-onset seizures, complementing evidence from clinical trials.