NeuroMetrix (United States)

This rate of expediting artificial intelligence (AI), big data analytics, and cyber-physical systems has restructured the operational functions of industries, bringing new opportunities of intelligent automation systems. This paper examines the possibilities of optimising operational roles by structured automation architectures that combine machine learning (ML), Internet of Things (IoT) networks, enterprise systems and decision support engines. The discussion is based on autonomous systems evidence, Industry 4.0 interoperability model evidence, big-data-driven financial decision platform evidence, ERP optimisation research evidence and sector-specific automation case evidence. Studies of autonomous systems have shown the capacity of intelligent agents to enhance perception, decision-making and effectiveness of coordination in dynamic systems [1], and interoperability models have shown the architectural concepts required of scalability of automation across the industrial layers [2]. The works done in operations and supply chain management show that AI improves predictability, precision in schedules, and visibility in complex networks [3], [4], [9]. Accounting, auditing, and pharmacovigilance evidence has demonstrated intelligent RPA to be able to optimise workflows involving judgement processes when it is supplemented with ML and natural language technologies [7], [8]. These insights are summarised in this article into a single automation evaluation, as to the extent, limitations and the job role changes that are made possible by intelligent automation. The resultant analysis illustrates that data centric automation frameworks generate significant operational value such as a greater degree of accuracy, quicker cycle times, and greater decision relevance.

To develop and validate a vendor-agnostic, motion-insensitive proton-density fat-fraction (PDFF) quantification method. Flip-angle-modulated (FAM) 2D chemical-shift-encoded (CSE) MRI for PDFF quantification was implemented in both the vendor-agnostic platform Pulseq ("Pulseq-FAM") and one vendor-specific platform ("GE-specific FAM"). These implementations were distributed to four sites with twelve MR systems of three vendors (Siemens/GE/Philips) and field strengths (0.55T/1.5T/3T). A sequentially-shipped 16-vial phantom (PDFF = 0%-30%/T1water = 200-1400 ms) underwent confounder-corrected PDFF mapping with commercial 3D-CSE methods and GE-specific FAM as available on each system, and Pulseq-FAM on every system. To assess bias, phantom PDFF measurements were compared to reference. Between-system variance was evaluated with linear mixed-effects modeling. Different volunteers were also imaged at each site to assess free-breathing PDFF mapping feasibility. A prospective single-site volunteer study was also conducted. Adult patients and children were imaged with breath-held 3D-CSE and free-breathing GE-specific and Pulseq-FAM. Radiologists evaluated images for overall quality and motion artifacts. To assess bias, Pulseq-FAM PDFF measurements were compared to 3D-CSE and GE-specific FAM. Test-retest repeatability was assessed by re-imaging after repositioning. Between-field-strength reproducibility was assessed at 1.5T and 3.0T. In the multi-center study, Pulseq-FAM showed reduced T1-bias and between-system variability versus 3D-CSE in phantom PDFF measurements, and free-breathing feasibility in volunteers. In the single-site volunteer study (N = 57), Pulseq-FAM improved image quality and motion artifacts versus 3D-CSE (p < 0.01). Pulseq-FAM showed excellent agreement with 3D-CSE (95% limits-of-agreement (LoA) = 3.4% PDFF) and GE-specific FAM (LoA = 2.0%). Pulseq-FAM showed excellent repeatability (repeatability coefficient (RC) = 1.6% PDFF) and between-field-strength reproducibility (reproducibility coefficient (RDC) = 2.4%) versus 3D-CSE (RC = 2.7%/RDC = 3.4%; differences p < 0.05). Pulseq-FAM enables accurate, reproducible, vendor-agnostic, and motion-insensitive PDFF quantification in adults and children.

Abstract Remaining Useful Life (RUL) prediction is central to maintenance planning in the manufacturing sector. It refers to the study of when some specified equipment or system is likely to fail, based on its current operating characteristics and therefore allows engineers to schedule maintenance, avoid unplanned disruptions and streamline operations, thereby reducing costs and enhancing efficiency. In this paper, we present several data-driven applications for system health monitoring with a focus on oilfield applications, such as artificial lift systems. A holistic and reliably predictive view of RUL requires addressing the effect of various factors on system degradation, such as environment variables and the prior history of operation, both of which can run into thousands of variables with often confounding effects. We describe one such approach for RUL prediction that can potentially highlight causative factors for equipment failure and degradation, that is known as survival analyses. These approaches are paired with workflows that assess the effect of uncertainty on the predictions of equipment health and RUL. We demonstrate the application of survival analyses with a presentation of the prognostic or causative factors driving system degradation. Our applications specifically include field-wide RUL analyses for artificial lift systems, including ESPs. For problems such as these, model-based RUL prediction becomes challenging simply because the formulation of a system model with the complexities in the underlying physics becomes intractable. This is where appropriate feature engineering is critical. Our work presents an argument for increased adoption of RUL prediction methods in the petroleum industry. Failures and unwarranted downtime are commonplace, and the methods presented here have the advantage of being employed by even smaller, regional operators and service companies. To our knowledge, this is the first discussion of Remaining Useful Life prediction methods combined with feature engineering.

BackgroundEffective cancer immunotherapy is driven by robust T cell responses against neoantigens. A sufficient number of neoantigens is crucial to stimulate at least 2–3 cancer-specific T cell responses that can home into the tumor microenvironment and exert therapeutic activity. Current clinical trials support these principles, showing promising results with combinations of approved immunotherapies, including anti-PD1 antibodies. However, most existing delivery systems rely on single-platform approaches, such as long peptides, plasmid DNA or mRNA-LNPs, which often result in limited and short-lived immune responses.Materials and MethodsTo address the limitations of conventional delivery platforms, we investigated a synthetic approach combining the Hermes™ lipopolyplex nanoparticle platform encapsulating mRNA in combination with delivery of synthetic linear hairpin (hp)DNA™. Synthesis of hpDNA™ is fully enzymatic, facilitating fast production of DNA vaccine devoid of bacterial backbone with improved quality over traditional plasmid fermentation processes. Meanwhile, Hermes™ nanoparticles exploit a cationic ligand, combined with traditional lipids, to drive payload condensation and extend shelf life at refrigerator temperature.ResultsTo explore the efficacy of these technologies, vaccines targeting 20 neoantigens of the MC38 tumor model were generated and combined in an innovative heterologous prime-boost protocol. This strategy partners the potent but transient nature of mRNA with synthetic DNA to enhance immunogenicity and durability of the anti-tumor response. While hpDNA encoding a large number of neoantigens is effective in inducing significant immune responses and antitumor activity, its combination with the new mRNA-Hermes™ formulation provide a faster T cell induction and improved tumor responses. This new therapeutic protocol represents the first example of a completely synthetic heterologous prime-boost vaccination protocol for cancer therapy. The results from our study suggest that the heterologous prime-boost regimen induces a polyfunctional and polyspecific T cell response against neoantigens, offering several advantages in the clinical setting. Responses in the MC38 tumor model are characterized by long-lasting activity and significant anti-tumor effects. The heterologous synthetic platform demonstrates a potent ability to elicit sustained immune responses, surpassing the performance of individual delivery systems including plasmid DNA or mRNA-LNP-based systems.ConclusionsIn conclusion, our findings provide compelling evidence that a synthetic biology-based heterologous prime-boost strategy is feasible and may offer an alternative valid immunotherapy platform. This approach holds significant potential for developing more effective and durable cancer immunotherapies, offering new hope for patients with challenging malignancies.I. Esposito: None. C. Tonini: None. A. Bianchi: None. E. Young: None. A. Dhir: None. M. Esposito: None. L. Aurisicchio: None. A. Walker: None. F. Palombo: None.

Abstract The MYC gene, with its oncogenic potential, has long been a formidable challenge in conventional drug discovery efforts, and its critical role in cancer progression and resistance has underscored the need for innovative therapeutic strategies. Here we demonstrate the capabilities of the Ribometrix RNA-targeting platform to modulate the c-MYC mRNA with small molecules with the aim of reducing c-MYC protein levels. Using our comprehensive platform of RNA-targeting drug discovery tools and analyses including chemical probing, structure modeling, high-purity RNA production, high-throughput screening, and biophysical characterization, we evaluated the potential to directly target c-MYC mRNA using small molecules. Our analysis revealed six high-confidence structured elements throughout the c-MYC mRNA expected to harbor tertiary structures amenable to drug-like ligand binding. Leveraging the multifaceted chemical probing and structure modeling components of our platform, we confirmed that our in vitro-transcribed RNA screening constructs adopt the same structures found in endogenous cellular c-MYC transcripts. After large-scale in vitro RNA preparation, we subjected four of these RNA elements to high-throughput screens of chemically diverse drug-like libraries using mass spectrometry affinity-selection that identified multiple chemical series with sub-micromolar affinity. We validated binding affinity using orthogonal methods including isothermal titration calorimetry and NMR. To eliminate pan-binding ligands, we evaluated of selectivity against a panel of non-target mRNA structures. Importantly, these compounds lead to rapid (4 hr) reduction of MYC protein levels in a small cell lung cancer cell line, DMS-273, with no cellular toxicity at 72 hr suggesting an on-target effect. Employing our suite of chemical probing tools, we confirmed that compounds from multiple series engage the c-MYC mRNA in cells. Medicinal chemistry efforts are ongoing to increase potency and define the precise mechanism of action in clinically relevant models. The discovery of these c-MYC mRNA-binding small molecules not only validates the utility of the Ribometrix RNA-targeted small molecule discovery platform but also showcases its potential in tackling traditionally 'undruggable' targets and provides a promising avenue for developing novel anti-cancer therapies. As we learn more about the intricacies of c-MYC mRNA biology and further refine effective small molecules, our results provide practical insights into previously-unknown vulnerabilities of MYC and present tangible opportunities for advancing targeted cancer therapies. Citation Format: Matthew J. Smola, Krista Marran, Sarah E. Thompson, Brittani Patterson, Roheeth K. Pavana, Caleb Sutherland, Jessica A. Sorrentino, Katherine D. Warner. Leveraging an RNA-targeting platform for the discovery of cell-active c-MYC mRNA-binding small molecules [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 680.

Abstract Woody plant encroachment (WPE) is a global trend that occurs in many biomes, including savannas, and accelerates with fire suppression. Since WPE can result in increased storage of soil organic carbon (SOC), fire management, which may include fire suppression, can improve ecosystem carbon (C) sequestration in savannas. At our study site in Kruger National Park, South Africa, we used a long‐term (~70 year) fire experiment to study the drivers and consequences of changes in woody cover (trees and shrubs) on SOC sequestration. We surveyed four fire manipulation treatments, replicated at eight locations within the park: annual high‐intensity burns, triennial high (dry season) and low‐intensity (wet season) burns, and fire exclusion, to capture the range of fire management scenarios under consideration. The changes in woody cover were calculated over a period similar to the experiment's duration (~80 years) using aerial photographs (1944–2018). Soils were analysed to 30 cm depth for SOC and δ 13 C, under and away from the tree canopy to isolate local‐ and landscape‐level effects of WPE on SOC. The largest increases in woody cover occurred with fire exclusion. We found that plots with higher increases in woody cover also had higher SOC. However, trees were not the only contributor to SOC gains, sustained high inputs of C 4 ‐derived C (grasses), even under canopies in fire suppression plots, contributed significantly to SOC. We observed little difference in SOC sequestration between cooler triennial (wet season) burns and fire suppression. Synthesis . Grass input to soil organic carbon (SOC) remained high across the full range of woody cover created by varying burning regimes. The total SOC stocks stored from tree input only matched grass‐derived SOC stocks after almost 70 years of fire exclusion. Our results point to C 4 grasses as a resilient contributor to SOC under altered fire regimes and further challenge the assumption that increasing tree cover, either through afforestation schemes or fire suppression, will result in large gains in C sequestration in savanna soils, even after 70 years.

INTRODUO:As doenas do aparelho geniturinrio so a quarta causa de internaes da populao indgena em pronto-socorro na regio Norte do Brasil.Este grupo de condies diz respeito a problemas renais, ureterais, vesicais e do aparelho genital de ambos os sexos, relacionadas a doenas inflamatrias, funcionais, anatmicas, entre outras.Tais doenas apresentam diferentes distribuies comparando atendimentos eletivos e de urgncia, tendo relevncia clnica em ambos os casos.OBJETIVO: Comparar as internaes por doenas do aparelho geniturinrio da populao indgena na regio Norte, entre casos atendidos em pronto-socorro e os eletivos, no perodo de 2017 a 2023.MTODOS: Este um estudo transversal e retrospectivo de 6 anos com dados pblicos do Sistema nico de Sade (SUS), sendo informaes comparativas entre internaes eletivas e de emergncia por doenas do

Abstract Introduction Tonic motor activation (TOMAC) is a noninvasive medical device therapy that has been shown to reduce symptoms of refractory restless legs syndrome (RLS). Here, we evaluated the effects of TOMAC on subjective sleep quality. Methods One hundred and thirty-three adults with primary moderate-severe refractory RLS were 1:1 randomized to TOMAC or sham control for 4 weeks. Participants were instructed to self-administer a 30-minute TOMAC session bilaterally over the head of the fibula to electrically stimulate the peroneal nerve whenever RLS symptoms presented. Sleep quality was assessed through prespecified analysis of changes to Medical Outcomes Study Sleep Scale (MOS-Sleep) Sleep Problems Index I (MOS-I) and II (MOS-II) scores from baseline to Week 4. Post hoc t-tests were conducted on each MOS-Sleep item included in the MOS-I or MOS-II. Results MOS-I score changed by -11.8 points (48.5 to 36.7) for TOMAC and -2.8 points for sham (44.5 to 41.7), a difference of -9.0 points (p&amp;lt; 0.001). MOS-II score changed by -13.7 points (52.5 to 38.9) for TOMAC and -4.0 points for sham (48.3 to 44.3), a difference of -9.7 points (p&amp;lt; 0.001), which exceeds the proposed minimal clinically important difference (MCID) of 6 points. Post hoc analysis demonstrated statistically significant reductions in trouble falling asleep (item #7; -34.0% for TOMAC vs. -5.8% for sham; p&amp;lt; 0.01) and trouble staying awake during the day (item #9; -33.6% for TOMAC vs. -2.1% for sham; p&amp;lt; 0.05), which exceeded the standard MCID threshold of 30% relative to baseline. Conclusion For adults with refractory RLS, TOMAC improved subjective sleep quality, sleep latency, and daytime somnolence relative to sham control. Each of these improvements represented clinically important improvements. These data demonstrate that TOMAC can provide meaningful improvements to sleep quality and daytime functioning for patients with refractory RLS. Support (if any) Funding was provided by Noctrix Health, Inc.