12 research outputs found
LADDIE
No full text180 pairs of depth and ambient images captured with a Xenomatix XenoLidar Xact LiDAR sensor.
Images were captured in various locations in and around Edinburgh (UK).
Depth images are 16-bit with each pixel value corresponding to depth in centimeters.
Ambient images are 8-bit grey-scale
Guided direct time-of-flight Lidar for self-driving vehicles
No full textSelf-driving vehicles demand efficient and reliable depth-sensing technologies. Lidar, with its capacity for long-distance, high-precision measurement, is a crucial component in this pursuit. However, conventional mechanical scanning implementations suffer from reliability, cost, and frame rate limitations. Solid-state lidar solutions have emerged as a promising alternative, but the vast amount of photon data processed and stored using conventional direct time-of-flight (dToF) prevents long-distance sensing unless power-intensive partial histogram approaches are used.
This research introduces a pioneering ‘guided’ dToF approach, harnessing external guidance from other onboard sensors to narrow down the depth search space for a power and data-efficient solution. This approach centres around a dToF sensor in which the exposed time widow of independent pixels can be dynamically adjusted. A pair of vision cameras are used in this demonstrator to provide the guiding depth estimates.
The implemented guided dToF demonstrator successfully captures a dynamic outdoor scene at 3 fps with distances up to 75 m. Compared to a conventional full histogram approach, on-chip data is reduced by over 25 times, while the total laser cycles in each frame are reduced by at least 6 times compared to any partial histogram approach. The capability of guided dToF to mitigate multipath reflections is also demonstrated.
For self-driving vehicles where a wealth of sensor data is already available, guided dToF opens new possibilities for efficient solid-state lidar
LiDAR Ambient and Depth Data Images in Edinburgh (LADDIE)
No full text180 pairs of depth and ambient images captured with a Xenomatix XenoLidar Xact LiDAR sensor. Images were captured in various locations in and around Edinburgh (UK). Depth images are 16-bit with each pixel value corresponding to depth in centimeters. Ambient images are 8-bit grey-scale
Guided Direct Time-of-Flight Lidar Using Stereo Cameras for Enhanced Laser Power Efficiency
No full textSelf-driving vehicles demand efficient and reliable depth-sensing technologies. Lidar, with its capability for long-distance, high-precision measurement, is a crucial component in this pursuit. However, conventional mechanical scanning implementations suffer from reliability, cost, and frame rate limitations. Solid-state lidar solutions have emerged as a promising alternative, but the vast amount of photon data processed and stored using conventional direct time-of-flight (dToF) prevents long-distance sensing unless power-intensive partial histogram approaches are used. In this paper, we introduce a groundbreaking ‘guided’ dToF approach, harnessing external guidance from other onboard sensors to narrow down the depth search space for a power and data-efficient solution. This approach centers around a dToF sensor in which the exposed time window of independent pixels can be dynamically adjusted. We utilize a 64-by-32 macropixel dToF sensor and a pair of vision cameras to provide the guiding depth estimates. Our demonstrator captures a dynamic outdoor scene at 3 fps with distances up to 75 m. Compared to a conventional full histogram approach, on-chip data is reduced by over twenty times, while the total laser cycles in each frame are reduced by at least six times compared to any partial histogram approach. The capability of guided dToF to mitigate multipath reflections is also demonstrated. For self-driving vehicles where a wealth of sensor data is already available, guided dToF opens new possibilities for efficient solid-state lidar
Laser Power Efficiency of Partial Histogram Direct Time-of-Flight Lidar Sensors
No full textOn-chip capacity for storing temporal photon data in direct time-of-flight (dToF) lidar sensors is limited. This has prompted the development of various partial histogram approaches to reduce the amount of data stored on-chip. The aim of this paper is to inform sensor design by providing a taxonomy of these approaches, models for evaluating their impact on system laser power and identification of additional trade-offs which must be considered. All published on-chip partial histogram lidar approaches to-date are reviewed and two main categories are established: zooming and sliding. A means of evaluating any specific configuration based on its histogram reduction ratio (HRR) is also established. To quantitatively evaluate partial histogram approaches, a model to determine the minimum number of required laser cycles is developed. Both zooming and sliding are compared to an ideal baseline using this model, in order to establish a laser power penalty benchmark for each approach. These are evaluated over a range of real-world design conditions for two contrasting designs: short-range indoor and long-range outdoor. In general, a sliding approach is found to be the most laser power-efficient for long-range outdoor applications, while a zooming approach becomes increasingly more effective under low ambient conditions. Power efficient cycle-scaled variations on the conventional zooming and sliding approaches are introduced. These are shown to consistently reduce the laser power penalty across all tested design conditions. It is also shown that a cycle-scaled sliding histogram approach can be adopted to reduce the required on-chip histogram storage capacity by half, with almost no additional laser power penalty. Finally, a qualitative discussion of zooming and sliding compares additional key design considerations such as sensitivity to motion artefacts
Diabetes in acute coronary syndrome patients: do we see only the tip of the iceberg?
No full textAim of the study: To analyse the influence of glycoregulation in pts. with known or newly detected diabetes, on in-hospital morbidity/mortality in patients with acute coronary syndrome. Methods: randomly selected ACS patients were analysed for: stress glycaemia, HgbA1c, risk profile, lipid profile, SINTAX score, TIMI flow, LV function and in-hospital morbidity/mortality. We comparatively analysed pts. based on the level of HgbA1c (⩾ 6,5% vs 6.5%). Mean values of HgbA1c and stress glycaemia were as follows: NonD - 5.19±0.56 and 6.82±1.87; PD - 5.99±0.19 and 8.32±3.17; ND - 8.19±1.15 and 17.68.19±1.15; CD - 5.79±0.55 and 8.89±4.38; and UD - 9.36±1.33 and 16.23±6.24; (ANOVA p >0.000). No significant difference was found between NonD and CD pts., and between ND and UD (high in the last two), but there was significant difference in HgbA1c (p0.000, Kappa agreement (0.516; sig p>0.000). TG levels were increased only in UD, and ND groups: 1.93±1.06, and 2.36±1.22, (ANOVA p=0.026, Tukey test ND vs NonD p=0.050; and vs PD p=0.016), without significant difference in other lipid fractions. Mean SINTAX score was 15.45±8.2, without significant inter-gorup differences. TIMI flow before PCI significantly differed across the groups, the lowest being in ND - 0.14±0.36 and PD - 1.13±1.42 pts. (group value 1.37±1.42; ANOVA p=0.001; Tukey test: NonD vs ND 0.000; and 0.043 vs CD). Mean EF was 51.51±8.5, without significant inter-group difference. 29 in-hospital events in 22 (19%) patients were registered: 7.7% arrhythmias, 6.9% heart failure, 3.4% GIT bleedings, and 2.6% CVI. In-hospital mortality was 4.3%. In multivariate logistic regression analysis, ejection fraction, stress glycaemia, and HgbA1c were identified as independent predictors of in-hospital outcome. Conclusion: High prevalence of unknown diabetes in ACS patients exists, leading to worse CAD, even in comparison with pts with known, well controlled diabetes. Stress glycaemia, HgbA1c and ejection fraction are independent predictors of in-hospital morbidity/mortality
Anemia, renal impairment and in-hospital mortality, in acute worsening chronic heart failure patients
No full textAim of the study: To analyze the impact of anemia and renal impairment on in-hospital mortality(IHD), in patients with acute worsening chronic heart failure. Methods: 232 randomly selected patients with symptoms of HF were retrospectively analyzed. Analyzed variables: gender, age, risk factors and co-morbidities: HTA, HLP, DM, COPD, CAD, PVD, CVD, anemia(defined as Hgb ≤10mg/dl), renal failure. Measured variables: systolic and diastolic BP, Hgb, sodium, BUN, creatinine, length of hospital stay and IHD. Comparative analysis was performed between patients with in-hospital mortality(IHD) and survivors, as a function of anemia and renal impairment. Statistical analysis: descriptive and comparative analysis, t-test, Chi square, univariate (binary logistic and linear regression and multivariate linear regression(stepwise backward). Results: Mean age 69.6±11.4, 102(44%)females and 130(56%) males, with females being significantly older 72.6±12.5 vs. 67.7±10.2(p=0.002), with significantly higher SBP, DBP and sodium level (p=0.003; 0.002 and 0.028 respectively), and males having HTA more often OR 1.3; p=0.017. Mean hospital stay was 6.8±5.8 days, with significant difference between IHD and non IHD group(7.9±4.5 vs. 3.8±7.9; p=0.000), with the highest mortality during the first (37.3%) and second hospital day (44.1%). 44 pts.(19%) had anemia, 31(13.4%) had known Chronic Renal Failure(CRF), and 59(25.4%) had IHD. Anemia was significantly associated with IHD (Chi square 6,36, sig 0.012, Exp B 2.48, sig 0.010), meaning pts. with anemia had 2,5 times greater risk for IHD. CRF per se, was not associated with IHD. Univariate linear regression identified creatinine(R square .032, beta .180, sig 0.006), and BUN(R square .034, beta .184, sig 0.005), as predictors of IHD. Multivariate stepwise regression model(anemia, HRF, Hgb, BUN, creatinine, sodium) at step 3(mean square .799, sig 0.002), identified two independent predictors Hgb(beta -.148, sig 0.028), and BUN(beta .163, sig 0.055). Multivariate model that included other known predictors of IHD(EF%, SBP, DBP, HRF, CAD, anemia, Hgb, BUN, creatinine, sodium) at step 8(mean square 1.537, sig 0.000), identified four independent predictors: EF%(beta -.204, sig 0.002), SBP(beta -.130, sig 0.052) as markers of systolic dysfunction and again anemia(Exp B 2.2.06, sig 0.041), and BUN(beta .200, sig 0.002). Conclusion: Anemia and renal impairment are well known comorbidities associated with HF that have great impact on course of HF. We confirmed that anemia and BUN, are significantly independent predictors of in hospital mortality in acute worsening CHF
Author Correction: GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19 (Nature, (2023), 617, 7962, (764-768), 10.1038/s41586-023-06034-3)
No full textCorrection to: Nature Published online 17 May 2023 In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article
Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity
No full textThe combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)
Whole-genome sequencing reveals host factors underlying critical COVID-19
No full textAltres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease
