Explore the vast range of titles available.

The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lies in a modular and scalable cognitive IoT hardware platform, which leverages microserver technology to enable users to configure the hardware to meet the requirements of a diverse array of applications. Heterogeneous computing is used to boost performance and energy efficiency. In addition, the full spectrum of hardware accelerators is integrated, providing specialized ASICs as well as FPGAs for reconfigurable computing. The project's contributions span across trusted computing, remote attestation, and secure execution environments, with the ultimate goal of facilitating the design and deployment of robust and efficient AIoT systems. The overall architecture is validated on use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. Ten additional use cases are integrated via an open call, broadening the range of application areas.

The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.

Human personality is 30–60% heritable according to twin and adoption studies. Hundreds of genetic variants are expected to influence its complex development, but few have been identified. We used a machine learning method for genome-wide association studies (GWAS) to uncover complex genotypic–phenotypic networks and environmental interactions. The Temperament and Character Inventory (TCI) measured the self-regulatory components of personality critical for health (i.e., the character traits of self-directedness, cooperativeness, and self-transcendence). In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified five clusters of people with distinct profiles of character traits regardless of genotype. Third, we found 42 SNP sets that identified 727 gene loci and were significantly associated with one or more of the character profiles. Each character profile was related to different SNP sets with distinct molecular processes and neuronal functions. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of 95% of the 42 SNP sets in healthy Korean and German samples, as well as their associations with character. The identified SNPs explained nearly all the heritability expected for character in each sample (50 to 58%). We conclude that self-regulatory personality traits are strongly influenced by organized interactions among more than 700 genes despite variable cultures and environments. These gene sets modulate specific molecular processes in brain for intentional goal-setting, self-reflection, empathy, and episodic learning and memory.

Phylogenetic, developmental, and brain-imaging studies suggest that human personality is the integrated expression of three major systems of learning and memory that regulate (1) associative conditioning, (2) intentionality, and (3) self-awareness. We have uncovered largely disjoint sets of genes regulating these dissociable learning processes in different clusters of people with (1) unregulated temperament profiles (i.e., associatively conditioned habits and emotional reactivity), (2) organized character profiles (i.e., intentional self-control of emotional conflicts and goals), and (3) creative character profiles (i.e., self-aware appraisal of values and theories), respectively. However, little is known about how these temperament and character components of personality are jointly organized and develop in an integrated manner. In three large independent genome-wide association studies from Finland, Germany, and Korea, we used a data-driven machine learning method to uncover joint phenotypic networks of temperament and character and also the genetic networks with which they are associated. We found three clusters of similar numbers of people with distinct combinations of temperament and character profiles. Their associated genetic and environmental networks were largely disjoint, and differentially related to distinct forms of learning and memory. Of the 972 genes that mapped to the three phenotypic networks, 72% were unique to a single network. The findings in the Finnish discovery sample were blindly and independently replicated in samples of Germans and Koreans. We conclude that temperament and character are integrated within three disjoint networks that regulate healthy longevity and dissociable systems of learning and memory by nearly disjoint sets of genetic and environmental influences.

Experimental studies of learning suggest that human temperament may depend on the molecular mechanisms for associative conditioning, which are highly conserved in animals. The main genetic pathways for associative conditioning are known in experimental animals, but have not been identified in prior genome-wide association studies (GWAS) of human temperament. We used a data-driven machine learning method for GWAS to uncover the complex genotypic–phenotypic networks and environmental interactions related to human temperament. In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified 3 clusters of people with distinct temperament profiles measured by the Temperament and Character Inventory regardless of genotype. Third, we found 51 SNP sets that identified 736 gene loci and were significantly associated with temperament. The identified genes were enriched in pathways activated by associative conditioning in animals, including the ERK, PI3K, and PKC pathways. 74% of the identified genes were unique to a specific temperament profile. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of the 51 Finnish SNP sets in healthy Korean (90%) and German samples (89%), as well as their associations with temperament. The identified SNPs explained nearly all the heritability expected in each sample (37–53%) despite variable cultures and environments. We conclude that human temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term memory.

The aim of this study is to deploy machine learning (ML) classification methods to detect defective regions in additive manufacturing, colloquially known as 3D printing, particularly for the laser-based powder bed fusion process. A custom-designed test specimen composed of 316L was manufactured using EOS M 290 machine. Multinomial logistic regression (MLR), artificial neural network (ANN), and convolutional neural network (CNN) classification techniques were applied to train the ML models using optical tomography infrared images of each additively manufactured layer of test specimen. Based on the trained MLR, ANN, and CNN classifiers, the ML models predict whether the manufactured layer is standard or defective, yielding five classes. Defective layers were classified into two classes for lack of fusion and two classes for keyhole porosity. The supervised approach yielded impeccable accuracy (>99%) for all three classification methods, however CNN inherited the highest degree of performance with 100% accuracy for independent test dataset unfamiliar to the model for unbiased evaluation. The high performance and low cost of computing observed in this work can have the potential to detect and eliminate defective regions by tuning the processing parameters in real time resulting in significantly decreased costs, lead-time, and waste. The proposed quality control can enable mass adoption of additive manufacturing technologies in a vast number of industries for critical components that are design- and shape- agnostic.

The Synergy between PCI with Taxus and Cardiac Surgery (SYNTAX) trial was a non-inferiority trial that compared percutaneous coronary intervention (PCI) using first-generation paclitaxel-eluting stents with coronary artery bypass grafting (CABG) in patients with de-novo three-vessel and left main coronary artery disease, and reported results up to 5 years. We now report 10-year all-cause death results. The SYNTAX Extended Survival (SYNTAXES) study is an investigator-driven extension of follow-up of a multicentre, randomised controlled trial done in 85 hospitals across 18 North American and European countries. Patients with de-novo three-vessel and left main coronary artery disease were randomly assigned (1:1) to the PCI group or CABG group. Patients with a history of PCI or CABG, acute myocardial infarction, or an indication for concomitant cardiac surgery were excluded. The primary endpoint of the SYNTAXES study was 10-year all-cause death, which was assessed according to the intention-to-treat principle. Prespecified subgroup analyses were performed according to the presence or absence of left main coronary artery disease and diabetes, and according to coronary complexity defined by core laboratory SYNTAX score tertiles. This study is registered with ClinicalTrials.gov, NCT03417050. From March, 2005, to April, 2007, 1800 patients were randomly assigned to the PCI (n=903) or CABG (n=897) group. Vital status information at 10 years was complete for 841 (93%) patients in the PCI group and 848 (95%) patients in the CABG group. At 10 years, 248 (28%) patients had died after PCI and 212 (24%) after CABG (hazard ratio 1·19 [95% CI 0·99–1·43], p=0·066). Among patients with three-vessel disease, 153 (28%) of 546 had died after PCI versus 114 (21%) of 549 after CABG (hazard ratio 1·42 [95% CI 1·11–1·81]), and among patients with left main coronary artery disease, 95 (27%) of 357 had died after PCI versus 98 (28%) of 348 after CABG (0·92 [0·69–1·22], pinteraction=0·023). There was no treatment-by-subgroup interaction with diabetes (pinteraction=0·60) and no linear trend across SYNTAX score tertiles (ptrend=0·20). At 10 years, no significant difference existed in all-cause death between PCI using first-generation paclitaxel-eluting stents and CABG. However, CABG provided a significant survival benefit in patients with three-vessel disease, but not in patients with left main coronary artery disease. German Foundation of Heart Research (SYNTAXES study, 5–10-year follow-up) and Boston Scientific Corporation (SYNTAX study, 0–5-year follow-up).

microRNAs (miRNAs or miRs) are short non-coding RNA molecules which have been shown to be dysregulated and released into the extracellular milieu as a result of many drug and non-drug-induced pathologies in different organ systems. Consequently, circulating miRs have been proposed as useful biomarkers of many disease states, including drug-induced tissue injury. miRs have shown potential to support or even replace the existing traditional biomarkers of drug-induced toxicity in terms of sensitivity and specificity, and there is some evidence for their improved diagnostic and prognostic value. However, several pre-analytical and analytical challenges, mainly associated with assay standardization, require solutions before circulating miRs can be successfully translated into the clinic. This review will consider the value and potential for the use of circulating miRs in drug-safety assessment and describe a systems approach to the analysis of the miRNAome in the discovery setting, as well as highlighting standardization issues that at this stage prevent their clinical use as biomarkers. Highlighting these challenges will hopefully drive future research into finding appropriate solutions, and eventually circulating miRs may be translated to the clinic where their undoubted biomarker potential can be used to benefit patients in rapid, easy to use, point-of-care test systems.

Background Recombinase (uvsY and uvsX) from bacteriophage T4 is a key enzyme for recombinase polymerase amplification (RPA) that amplifies a target DNA sequence at a constant temperature with a single-stranded DNA-binding protein and a strand-displacing polymerase. The present study was conducted to examine the effects of the N- and C-terminal tags of uvsY on its function in RPA to detect SARS-CoV-2 DNA. Methods Untagged uvsY (uvsY-Δhis), N-terminal tagged uvsY (uvsY-Nhis), C-terminal tagged uvsY (uvsY-Chis), and N- and C-terminal tagged uvsY (uvsY-NChis) were expressed in Escherichia coli and purified. RPA reaction was carried out with the in vitro synthesized standard DNA at 41 °C. The amplified products were separated on agarose gels. Results The minimal initial copy numbers of standard DNA from which the amplified products were observed were 6 × 10 5 , 60, 600, and 600 copies for the RPA with uvsY-Δhis, uvsY-Nhis, uvsY-Chis, and uvsY-NChis, respectively. The minimal reaction time at which the amplified products were observed were 20, 20, 30, and 20 min for the RPA with uvsY-Δhis, uvsY-Nhis, uvsY-Chis, and uvsY-NChis, respectively. The RPA with uvsY-Nhis exhibited clearer bands than that with either of other three uvsYs. Conclusions The reaction efficiency of RPA with uvsY-Nhis was the highest, suggesting that uvsY-Nhis is suitable for use in RPA.

Converging lines of evidence suggest that microglia are relevant to Parkinson's disease pathogenesis, justifying exploration of therapeutic agents thought to attenuate pathogenic microglial function. We sought to test the safety and efficacy of NLY01—a brain-penetrant, pegylated, longer-lasting version of exenatide (a glucagon-like peptide-1 receptor agonist) that is believed to be anti-inflammatory via reduction of microglia activation—in Parkinson's disease. We report a 36-week, randomised, double-blind, placebo-controlled study of NLY01 in participants with early untreated Parkinson's disease conducted at 58 movement disorder clinics in the USA. Participants meeting UK Brain Bank or Movement Disorder Society research criteria for Parkinson's disease were randomly allocated (1:1:1) to one of two active treatment groups (2·5 mg or 5·0 mg NLY01) or matching placebo, based on a central computer-generated randomisation scheme using permuted block randomisation with varying block sizes. All participants, investigators, coordinators, study staff, and sponsor personnel were masked to treatment assignments throughout the study. The primary efficacy endpoint for the primary analysis population (defined as all randomly assigned participants who received at least one dose of study drug) was change from baseline to week 36 in the sum of Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) parts II and III. Safety was assessed in the safety population (all randomly allocated participants who received at least one dose of the study drug) with documentation of adverse events, vital signs, electrocardiograms, clinical laboratory assessments, physical examination, and scales for suicidality, sleepiness, impulsivity, and depression. This trial is complete and registered at ClinicalTrials.gov, NCT04154072. The study took place between Jan 28, 2020, and Feb 16, 2023. 447 individuals were screened, of whom 255 eligible participants were randomly assigned (85 to each study group). One patient assigned to placebo did not receive study treatment and was not included in the primary analysis. At 36 weeks, 2·5 mg and 5·0 mg NLY01 did not differ from placebo with respect to change in sum scores on MDS-UPDRS parts II and III: difference versus placebo –0·39 (95% CI –2·96 to 2·18; p=0·77) for 2·5 mg and 0·36 (–2·28 to 3·00; p=0·79) for 5·0 mg. Treatment-emergent adverse events were similar across groups (reported in 71 [84%] of 85 patients on 2·5 mg NLY01, 79 [93%] of 85 on 5·0 mg, and 73 [87%] of 84 on placebo), with gastrointestinal disorders the most commonly observed class in active groups (52 [61%] for 2·5 mg, 64 [75%] for 5·0 mg, and 30 [36%] for placebo) and nausea the most common event overall (33 [39%] for 2·5 mg, 49 [58%] for 5·0 mg, and 16 [19%] for placebo). No deaths occurred during the study. NLY01 at 2·5 and 5·0 mg was not associated with any improvement in Parkinson's disease motor or non-motor features compared with placebo. A subgroup analysis raised the possibility of motor benefit in younger participants. Further study is needed to determine whether these exploratory observations are replicable. D&D Pharmatech—Neuraly.