The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data

HEP.TrkX Project: Deep Learning for Particle Tracking

Charged particle reconstruction in dense environments, such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms, such as the combinatorial Kalman Filter, have been used with great success in HEP experiments for years. However, these state-of-the-art techniques are inherently sequential and scale quadratically or worse with increased detector occupancy. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as FPGAs or GPUs. In this paper we present the evolution and performance of our recurrent (LSTM) and convolutional neural networks moving from basic 2D models to more complex models and the challenges of scaling up to realistic dimensionality/sparsity

Recombinant Lloviu virus as a tool to study viral replication and host responses

Next generation sequencing has revealed the presence of numerous RNA viruses in animal reservoir hosts, including many closely related to known human pathogens. Despite their zoonotic potential, most of these viruses remain understudied due to not yet being cultured. While reverse genetic systems can facilitate virus rescue, this is often hindered by missing viral genome ends. A prime example is Lloviu virus (LLOV), an uncultured filovirus that is closely related to the highly pathogenic Ebola virus. Using minigenome systems, we complemented the missing LLOV genomic ends and identified cis-acting elements required for LLOV replication that were lacking in the published sequence. We leveraged these data to generate recombinant full-length LLOV clones and rescue infectious virus. Similar to other filoviruses, recombinant LLOV (rLLOV) forms filamentous virions and induces the formation of characteristic inclusions in the cytoplasm of the infected cells, as shown by electron microscopy. Known target cells of Ebola virus, including macrophages and hepatocytes, are permissive to rLLOV infection, suggesting that humans could be potential hosts. However, inflammatory responses in human macrophages, a hallmark of Ebola virus disease, are not induced by rLLOV. Additional tropism testing identified pneumocytes as capable of robust rLLOV and Ebola virus infection. We also used rLLOV to test antivirals targeting multiple facets of the replication cycle. Rescue of uncultured viruses of pathogenic concern represents a valuable tool in our arsenal for pandemic preparedness

Haematological and immunological changes in cotton mill workers during their shift

SUMMARY. Objectives: To study the haematological and immunological changes in the blood of cotton mill workers during the course of their shift. Method: Blood samples were taken from 70 cotton mill workers before their shift and after 4 hours of cotton dust exposure on the first day of the working week. The following parameters were measured: red blood cells (RBC), haemoglobin (Hb), haematocrit (Ht), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), red cell distribution width (RDW), platelets (PLT), plateletcrit (PCT), mean platelet volume (MPV), platelet distribution width (PDW), white blood cells (WBC), absolute number and percentage of polymorphonuclear leucocytes, eosinophils and mononuclear leucocytes, IgG, IgA, IgE, IgM, CRP, a2 macroglobulin, haptoglobin, acute-a1glycoprotein, prealbumin, fibronectin, fibrinogen, prothrombin, plasminogen, antithrombin III, a1antitrypsin, C1q, C3c, C4, C5, B-factor, and circulating immune complexes (CIC). Screening was made for the specific IgE against cotton. The level of cotton dust in the workplace was measured by the method of vertical elutriator. Statistical analysis was performed using the paired t-test. Results: Following cotton dust exposure of four hours duration a statistically significant increase was observed in the absolute number and the percentage of polymorphonuclear leucocytes, and in MCHC, PLT, PCT, IgG, IgA, a2 macroglobulin, haptoglopin, fibronectin, fibrinogen, C3c, and C4. There was a statistically significant decrease in the absolute number and the percentage of lymphocytes, eosinophils and monocytes, and in RBC, Hb, Ht, MCV, RDW, C1q and B-factor. No statistically significant changes were observed in IgE, IgM, CRP, acute-a1glycoprotein, prealbumin, prothrombin, plasminogen CIC, antithrombin III, and C5. None of the workers tested positive for the specific IgE against cotton. The level of cotton dust was on average 0.8 mg/m3. Conclusion: After cotton dust exposure an immunological cascade takes place, probably with an acute phase reaction, activation of the classical and alternative complement pathways and possibly with a low degree of haemolysis. It is hypothesized that the main mechanism of the haematological and immunological changes observed in the population under study is a type II hypersensitivity response. Pneumon 2009, 22(4):315-330

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data

Efficacy and safety of early soluble urokinase plasminogen receptor plasma-guided anakinra treatment of COVID-19 pneumonia: a subgroup analysis of the SAVE-MORE randomised trialResearch in context

Summary: Background: The SAVE-MORE trial demonstrated that anakinra treatment in COVID-19 pneumonia with plasma soluble urokinase plasminogen activator (suPAR) levels of 6 ng/mL or more was associated with 0.36 odds for a worse outcome compared to placebo when expressed by the WHO-Clinical Progression Scale (CPS) at day 28. Herein, we report the results of subgroup analyses and long-term outcomes. Methods: This prospective, double-blind, randomised clinical trial, recruited patients with a confirmed SARS-CoV-2 infection, in need of hospitalisation, lower respiratory tract infection and plasma suPAR ≥6 ng/mL from 37 academic and community hospitals in Greece and Italy. Patients were 1:2 randomised to subcutaneous treatment with placebo or anakinra (100 mg) once daily for 10 days. Pre-defined subgroups of Charlson's comorbidity index (CCI), sex, age, level of suPAR, and time from symptom onset were analysed for the primary endpoint (overall comparison of distribution of frequencies of the scores from the WHO-CPS between treatments on day 28), by multivariable ordinal regression analysis in the intention to treat (ITT) population. This trial is registered with the EU Clinical Trials Register (2020-005828-11) and ClinicalTrials.gov (NCT04680949). Findings: Patients were enrolled between 23 December 2020 and 31 March 2021; 189 patients in the placebo arm and 405 patients in the anakinra arm were the ITT population. Multivariable analysis showed that anakinra treatment was accompanied by significantly lower odds for worse outcome compared to placebo at day 28 for all studied subgroups (CCI ≥ 2, OR: 0.34, 95% confidence intervals [CI] 0.22–0.50; CCI 9 ng/mL, OR: 0.35, 95% CI 0.19–0.66; suPAR 6–9 ng/mL, OR: 0.35, 95% CI 0.24–0.52; patients ≥65 years, OR: 0.41, 95% CI 0.25–0.66; and patients <65 years, OR: 0.29, 95% CI 0.19–0.45). The benefit was uniform, irrespective of the time from start of symptoms until the start of the study drug. At days 60 and 90, anakinra treatment had odds of 0.40 (95% CI 0.28–0.57) and 0.46 (95% CI 0.32–0.67) respectively, for a worse outcome compared to placebo. The costs of general ward stay, ICU stay, and drugs were lower with anakinra treatment. Interpretation: Anakinra represents an important therapeutic tool in the management of COVID-19 that may be administered in all subgroups of patients; benefits are maintained until day 90. Funding: Hellenic Institute for the Study of Sepsis; Swedish Orphan Biovitrum AB